On the public citation graph

Automatic Tuberculosis Screening Using Chest Radiographs

https://doi.org/10.1109/tmi.2013.2284099

0 signed · 721 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4757-3264-1openalex-snapshot

The Nature of Statistical Learning Theory

inferredcites →https://doi.org/10.1007/978-3-540-69812-8_61openalex-snapshot

Rotationally Invariant Hashing of Median Binary Patterns for Texture Classification

inferredcites →https://doi.org/10.1007/978-3-540-79547-6_30openalex-snapshot

CEDD: Color and Edge Directivity Descriptor: A Compact Descriptor for Image Indexing and Retrieval

inferredcites →https://doi.org/10.1007/978-3-642-13775-4_8openalex-snapshot

Fusing Shape Information in Lung Segmentation in Chest Radiographs

inferredcites →https://doi.org/10.1007/978-3-642-15711-0_81openalex-snapshot

Fusion of Local and Global Detection Systems to Detect Tuberculosis in Chest Radiographs

inferredcites →https://doi.org/10.1007/s10278-006-0626-4openalex-snapshot

Computer-Aided Nodule Detection on Digital Chest Radiography: Validation Test on Consecutive T1 Cases of Resectable Lung Cancer

inferredcites →https://doi.org/10.1007/s11263-006-7934-5openalex-snapshot

Graph Cuts and Efficient N-D Image Segmentation

inferredcites →https://doi.org/10.1016/0031-3203(95)00067-4openalex-snapshot

A comparative study of texture measures with classification based on featured distributions

inferredcites →https://doi.org/10.1016/j.acra.2006.04.007openalex-snapshot

Computer-aided Diagnosis for the Detection and Classification of Lung Cancers on Chest Radiographs

inferredcites →https://doi.org/10.1016/j.ejrad.2009.05.061openalex-snapshot

Computer-aided diagnosis in chest radiography: Beyond nodules

inferredcites →https://doi.org/10.1016/j.media.2005.02.002openalex-snapshot

Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database

inferredcites →https://doi.org/10.1016/j.media.2012.06.009openalex-snapshot

Clavicle segmentation in chest radiographs

inferredcites →https://doi.org/10.1016/j.patcog.2009.01.016openalex-snapshot

Dissimilarity-based classification in the absence of local ground truth: Application to the diagnostic interpretation of chest radiographs

inferredcites →https://doi.org/10.1049/ip-vis:20045185openalex-snapshot

Robust texture features for still-image retrieval

inferredcites →https://doi.org/10.1056/nejmoa0907847openalex-snapshot

Rapid Molecular Detection of Tuberculosis and Rifampin Resistance

inferredcites →https://doi.org/10.1097/00004424-196601000-00032openalex-snapshot

Computer-aided Diagnosis in Radiology

inferredcites →https://doi.org/10.1109/34.969114openalex-snapshot

Fast approximate energy minimization via graph cuts

inferredcites →https://doi.org/10.1109/42.974918openalex-snapshot

Computer-aided diagnosis in chest radiography: a survey

inferredcites →https://doi.org/10.1109/42.993132openalex-snapshot

Automatic detection of abnormalities in chest radiographs using local texture analysis

inferredcites →https://doi.org/10.1109/avss.2008.24openalex-snapshot

An Integrated System for Moving Object Classification in Surveillance Videos

inferredcites →https://doi.org/10.1109/cvpr.2005.177openalex-snapshot

Histograms of Oriented Gradients for Human Detection

inferredcites →https://doi.org/10.1109/embc.2012.6347110openalex-snapshot

Detecting tuberculosis in radiographs using combined lung masks

inferredcites →https://doi.org/10.1109/iccv.2009.5459207openalex-snapshot

An HOG-LBP human detector with partial occlusion handling

inferredcites →https://doi.org/10.1109/icif.2010.5711891openalex-snapshot

Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video

inferredcites →https://doi.org/10.1109/icpr.2010.632openalex-snapshot

Dual Channel Colocalization for Cell Cycle Analysis Using 3D Confocal Microscopy

inferredcites →https://doi.org/10.1109/iembs.2008.4649868openalex-snapshot

Epifluorescence-based quantitative microvasculature remodeling using geodesic level-sets and shape-based evolution

inferredcites →https://doi.org/10.1109/iembs.2009.5334012openalex-snapshot

Computer-aided renal cancer quantification and classification from contrast-enhanced CT via histograms of curvature-related features

inferredcites →https://doi.org/10.1109/isbi.2013.6556813openalex-snapshot

Multi-class regularization parameter learning for graph cut image segmentation

inferredcites →https://doi.org/10.1109/tbme.2010.2057509openalex-snapshot

A Hybrid Knowledge-Guided Detection Technique for Screening of Infectious Pulmonary Tuberculosis From Chest Radiographs

inferredcites →https://doi.org/10.1109/tgrs.2006.890579openalex-snapshot

GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries

inferredcites →https://doi.org/10.1109/tit.1962.1057692openalex-snapshot

Visual pattern recognition by moment invariants

inferredcites →https://doi.org/10.1109/tmi.2002.803121openalex-snapshot

Active shape model segmentation with optimal features

inferredcites →https://doi.org/10.1109/tmi.2007.896924openalex-snapshot

Minimal Shape and Intensity Cost Path Segmentation

inferredcites →https://doi.org/10.1109/tmi.2007.908130openalex-snapshot

Segmenting Lung Fields in Serial Chest Radiographs Using Both Population-Based and Patient-Specific Shape Statistics

inferredcites →https://doi.org/10.1109/tpami.2002.1017623openalex-snapshot

Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

inferredcites →https://doi.org/10.1109/tpami.2007.1110openalex-snapshot

Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

inferredcites →https://doi.org/10.1109/tpami.2011.155openalex-snapshot

Pedestrian Detection: An Evaluation of the State of the Art

inferredcites →https://doi.org/10.1109/wiamis.2008.24openalex-snapshot

FCTH: Fuzzy Color and Texture Histogram - A Low Level Feature for Accurate Image Retrieval

inferredcites →https://doi.org/10.1117/2.1201105.003732openalex-snapshot

Tuberculosis screening of chest radiographs

inferredcites →https://doi.org/10.1118/1.1312192openalex-snapshot

Automatic segmentation of lung fields in chest radiographs

inferredcites →https://doi.org/10.1145/1459359.1459577openalex-snapshot

Lire: lucene image retrieval

inferredcites →https://doi.org/10.1145/1631272.1631456openalex-snapshot

Caliph & Emir

inferredcites →https://doi.org/10.1145/1924559.1924614openalex-snapshot

Classification of cell cycle phases in 3D confocal microscopy using PCNA and chromocenter features

inferredcites →https://doi.org/10.1145/2072298.2072432openalex-snapshot

Content based image retrieval with LIRe

inferredcites →https://doi.org/10.1148/81.2.185openalex-snapshot

The Coding of Roentgen Images for Computer Analysis as Applied to Lung Cancer

inferredcites →https://doi.org/10.1148/radiol.11100153openalex-snapshot

Lung Nodules: Improved Detection with Software That Suppresses the Rib and Clavicle on Chest Radiographs

inferredcites →https://doi.org/10.1148/radiology.210.2.r99ja34307openalex-snapshot

Pulmonary Tuberculosis: The Essentials

inferredcites →https://doi.org/10.1148/rg.275065176openalex-snapshot

Tuberculosis: A Radiologic Review

inferredcites →https://doi.org/10.1259/bjr/82933343openalex-snapshot

Current status and future potential of computer-aided diagnosis in medical imaging

inferredcites →https://doi.org/10.2214/ajr.174.1.1740071openalex-snapshot

Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule

inferredcites →https://doi.org/10.2214/ajr.182.2.1820505openalex-snapshot

Improved Detection of Lung Nodules on Chest Radiographs Using a Commercial Computer-Aided Diagnosis System

inferredcites →https://doi.org/10.2316/p.2011.737-035openalex-snapshot

Application of Stepwise Binary Decision Classification for Reduction of False Positives in Tuberculosis Detection from Smeared Slides

inferredcites →https://doi.org/10.5588/ijtld.11.0004openalex-snapshot

High sensitivity of chest radiograph reading by clinical officers in a tuberculosis prevalence survey

inferredcites →https://doi.org/10.5588/ijtld.11.0425openalex-snapshot

Reexamining the role of radiography in tuberculosis case finding

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4057307openalex-extracted

Exploring Deep Learning Applications for COVID-19 Using X-Ray

inferred← citeshttps://doi.org/10.2139/ssrn.4577287openalex-extracted

Dense Affinity Matching for Few-Shot Segmentation

inferred← citeshttps://doi.org/10.2139/ssrn.4676192openalex-extracted

Lugsam: A Novel Framework for Integrating Text Prompts to Segment Anything Model (Sam) for Segmentation Tasks of Icu Chest X-Rays

inferred← citeshttps://doi.org/10.2139/ssrn.5007476openalex-extracted

Robust Lung Segmentation in Chest X-Ray Images Using Modified U-Net with Deeper Network and Residual Blocks

inferred← citeshttps://doi.org/10.1002/9781119282686.ch30openalex-snapshot

The Use of Mobile Chest X‐Rays for Tuberculosis Telemedicine

inferred← citeshttps://doi.org/10.1002/9781119818717.ch2openalex-snapshot

Thoracic Image Analysis Using Deep Learning

inferred← citeshttps://doi.org/10.1002/eng2.12906openalex-snapshot

Optimized tuberculosis classification system for chest X‐ray images: Fusing hyperparameter tuning with transfer learning approaches

inferred← citeshttps://doi.org/10.1002/ima.22556openalex-snapshot

A semantic contour based segmentation of lungs from chest x‐rays for the classification of tuberculosis using Naïve Bayes classifier

inferred← citeshttps://doi.org/10.1002/ima.22613openalex-snapshot

A novel machine learning‐based analytical framework for automatic detection of COVID ‐19 using chest X‐ray images

inferred← citeshttps://doi.org/10.1002/ima.22627openalex-snapshot

A novel and efficient deep learning approach for COVID ‐19 detection using X‐ray imaging modality

inferred← citeshttps://doi.org/10.1002/ima.70082openalex-snapshot

EDenseNetViT : Leveraging Ensemble Vision Transform Integrated Transfer Learning for Advanced Differentiation and Severity Scoring of Tuberculosis

inferred← citeshttps://doi.org/10.1002/mp.12561openalex-snapshot

Lung field segmentation using weighted sparse shape composition with robust initialization

inferred← citeshttps://doi.org/10.1002/mp.17481openalex-snapshot

Plug‐and‐play segment anything model improves nnUNet performance

inferred← citeshttps://doi.org/10.1002/mp.17984openalex-snapshot

A semi‐supervised multi‐connection contrastive learning framework for x‐ray lung segmentation based on mutual distillation

inferred← citeshttps://doi.org/10.1002/ppul.24431openalex-snapshot

Using deep‐learning techniques for pulmonary‐thoracic segmentations and improvement of pneumonia diagnosis in pediatric chest radiographs

inferred← citeshttps://doi.org/10.1007/174_2018_193openalex-snapshot

The Value in Artificial Intelligence

inferred← citeshttps://doi.org/10.1007/978-3-030-04164-9_69openalex-snapshot

Classification of Diseases on Chest X-Rays Using Deep Learning

inferred← citeshttps://doi.org/10.1007/978-3-030-23876-6_11openalex-snapshot

Online Variational Learning for Medical Image Data Clustering

inferred← citeshttps://doi.org/10.1007/978-3-030-32622-7_24openalex-snapshot

DTCo: An Ensemble SSL Algorithm for X-ray Classification

inferred← citeshttps://doi.org/10.1007/978-3-030-50420-5_47openalex-snapshot

Classification of Lung Diseases Using Deep Learning Models

inferred← citeshttps://doi.org/10.1007/978-3-030-59006-2_11openalex-snapshot

Tuberculosis Abnormality Detection in Chest X-Rays: A Deep Learning Approach

inferred← citeshttps://doi.org/10.1007/978-3-030-59725-2_56openalex-snapshot

Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-030-63355-4_24openalex-snapshot

State of the Art in Artificial Intelligence and Machine Learning Techniques for Improving Patient Outcomes Pertaining to the Cardiovascular and Respiratory Systems

inferred← citeshttps://doi.org/10.1007/978-3-030-64559-5_44openalex-snapshot

Pre-trained Convolutional Neural Network for the Diagnosis of Tuberculosis

inferred← citeshttps://doi.org/10.1007/978-3-030-69744-0_6openalex-snapshot

An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings

inferred← citeshttps://doi.org/10.1007/978-3-030-69951-2_9openalex-snapshot

EfficientNet-Based Convolutional Neural Networks for Tuberculosis Classification

inferred← citeshttps://doi.org/10.1007/978-3-030-71676-9_10openalex-snapshot

Deep Learning for Lung Disease Detection from Chest X-Rays Images

inferred← citeshttps://doi.org/10.1007/978-3-030-77091-4_3openalex-snapshot

Understanding Automatic Pneumonia Classification Using Chest X-Ray Images

inferred← citeshttps://doi.org/10.1007/978-3-030-84532-2_25openalex-snapshot

Classification of Pulmonary Diseases from X-ray Images Using a Convolutional Neural Network

inferred← citeshttps://doi.org/10.1007/978-3-030-87240-3_9openalex-snapshot

You only Learn Once: Universal Anatomical Landmark Detection

inferred← citeshttps://doi.org/10.1007/978-3-030-87602-9_6openalex-snapshot

Improving Tuberculosis Recognition on Bone-Suppressed Chest X-Rays Guided by Task-Specific Features

inferred← citeshttps://doi.org/10.1007/978-3-030-89691-1_2openalex-snapshot

A Machine Learning Based Approach for Estimation of the Lung Affectation Degree in CXR Images of COVID-19 Patients

inferred← citeshttps://doi.org/10.1007/978-3-030-93420-0_5openalex-snapshot

An Evaluation of Segmentation Techniques for Covid-19 Identification in Chest X-Ray

inferred← citeshttps://doi.org/10.1007/978-3-030-99619-2_3openalex-snapshot

XceptionUnetV1: A Lightweight DCNN for Biomedical Image Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-12053-4_2openalex-snapshot

Multimodal Cardiomegaly Classification with Image-Derived Digital Biomarkers

inferred← citeshttps://doi.org/10.1007/978-3-031-18523-6_7openalex-snapshot

GAN Latent Space Manipulation and Aggregation for Federated Learning in Medical Imaging

inferred← citeshttps://doi.org/10.1007/978-3-031-20056-4_5openalex-snapshot

Cross-Domain Few-Shot Semantic Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-28076-4_48openalex-snapshot

Towards Explainable AI on Chest X-Ray Diagnosis Using Image Segmentation and CAM Visualization

inferred← citeshttps://doi.org/10.1007/978-3-031-30788-1_2openalex-snapshot

Lung Segmentation of Chest X-Rays Using Unet Convolutional Networks

inferred← citeshttps://doi.org/10.1007/978-3-031-34960-7_16openalex-snapshot

Medical X-ray Image Classification Method Based on Convolutional Neural Networks

inferred← citeshttps://doi.org/10.1007/978-3-031-43898-1_41openalex-snapshot

A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications

inferred← citeshttps://doi.org/10.1007/978-3-031-43898-1_50openalex-snapshot

Joint Dense-Point Representation for Contour-Aware Graph Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-43898-1_68openalex-snapshot

Robust T-Loss for Medical Image Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-45249-9_26openalex-snapshot

Unsupervised Bias Discovery in Medical Image Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-031-46317-4_7openalex-snapshot

Causality-Inspired Source-Free Domain Adaptation for Medical Image Classification

inferred← citeshttps://doi.org/10.1007/978-3-031-67762-5_17openalex-snapshot

ResNet and ResNeSt-Based Deep-Learning Models for Accurate COVID-19 Detection from Chest X-ray Radiographs

inferred← citeshttps://doi.org/10.1007/978-3-031-71848-9_14openalex-snapshot

Deep Learning for Effective Tuberculosis Detection from Chest Radiographs: A Comparative Study

inferred← citeshttps://doi.org/10.1007/978-3-031-78198-8_8openalex-snapshot

Applying Layer-Wise Relevance Propagation on U-Net Architectures

inferred← citeshttps://doi.org/10.1007/978-3-031-81854-7_12openalex-snapshot

SwiftMedSAM: An Ultra-lightweight Prompt-Based Universal Medical Image Segmentation Model for Highly Constrained Environments

inferred← citeshttps://doi.org/10.1007/978-3-031-81854-7_6openalex-snapshot

A Light-Weight Universal Medical Segmentation Network for Laptops Based on Knowledge Distillation

inferred← citeshttps://doi.org/10.1007/978-3-031-81854-7_7openalex-snapshot

Taking a Step Back: Revisiting Classical Approaches for Efficient Interactive Segmentation of Medical Images

inferred← citeshttps://doi.org/10.1007/978-3-031-83790-6_12openalex-snapshot

Detection Model for Pulmonary TB on Augmented X-ray Images Enhanced Through Histogram Equalization

inferred← citeshttps://doi.org/10.1007/978-3-031-98688-8_18openalex-snapshot

DiNO-Diffusion: Scaling Medical Diffusion Models via Self-Supervised Pre-Training

inferred← citeshttps://doi.org/10.1007/978-3-032-01970-7_21openalex-snapshot

Detection of Pulmonary Tuberculosis Regions on Standard Radiographs Using Artificial Intelligence

inferred← citeshttps://doi.org/10.1007/978-3-032-04984-1_12openalex-snapshot

CF-Seg: Counterfactuals Meet Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-032-37556-8_30openalex-snapshot

Hierarchical Spatial and Channel Aggregation for Cross-Domain Few-Shot Segmentation

inferred← citeshttps://doi.org/10.1007/978-3-032-38098-2_19openalex-snapshot

CPR: $$\underline{{\textbf {C}}}$$hained $$\underline{{\textbf {P}}}$$erceptual $$\underline{{\textbf {R}}}$$efinement for Coarse-to-Fine Medical Image Classification

inferred← citeshttps://doi.org/10.1007/978-3-319-46723-8_28openalex-snapshot

Self-Transfer Learning for Weakly Supervised Lesion Localization

inferred← citeshttps://doi.org/10.1007/978-3-319-66179-7_51openalex-snapshot

X-Ray In-Depth Decomposition: Revealing the Latent Structures

inferred← citeshttps://doi.org/10.1007/978-3-319-67558-9_11openalex-snapshot

Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks

inferred← citeshttps://doi.org/10.1007/978-3-319-68560-1_55openalex-snapshot

Computer Aided Diagnosis of Pleural Effusion in Tuberculosis Chest Radiographs

inferred← citeshttps://doi.org/10.1007/978-3-319-91008-6_63openalex-snapshot

Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer

inferred← citeshttps://doi.org/10.1007/978-981-13-1208-3_11openalex-snapshot

Automatic Detection of Tuberculosis Using Deep Learning Methods

inferred← citeshttps://doi.org/10.1007/978-981-13-3765-9_5openalex-snapshot

Tuberculosis Detection Using Shape and Texture Features of Chest X-Rays

inferred← citeshttps://doi.org/10.1007/978-981-15-1518-7_32openalex-snapshot

DWT-LBP Descriptors for Chest X-Ray View Classification

inferred← citeshttps://doi.org/10.1007/978-981-15-6329-4_23openalex-snapshot

Segmented Lung Boundary Correction in Chest Radiograph Using Context-Aware Adaptive Scan Algorithm

inferred← citeshttps://doi.org/10.1007/978-981-15-6634-9_4openalex-snapshot

An Effective Vision Based Framework for the Identification of Tuberculosis in Chest X-Ray Images

inferred← citeshttps://doi.org/10.1007/978-981-15-9492-2_3openalex-snapshot

Continual Deep Learning Framework for Medical Media Screening and Archival

inferred← citeshttps://doi.org/10.1007/978-981-16-0749-3_64openalex-snapshot

Classification of Chest Diseases from X-ray Images on the CheXpert Dataset

inferred← citeshttps://doi.org/10.1007/978-981-16-2094-2_14openalex-snapshot

Deep Learning-Based Imbalanced Data Classification for Chest X-Ray Image Analysis

inferred← citeshttps://doi.org/10.1007/978-981-16-5747-4_72openalex-snapshot

An Overview of Pulmonary Tuberculosis Detection and Classification Using Machine Learning and Deep Learning Algorithms

inferred← citeshttps://doi.org/10.1007/978-981-19-1657-1_5openalex-snapshot

COVID-DeepNet: Deep Convolutional Neural Network Architecture Designed for Early Prognosis of COVID-19 Using Post-anterior View of Chest X-Rays

inferred← citeshttps://doi.org/10.1007/978-981-19-2541-2_41openalex-snapshot

Intelligent System for Diagnosis of Pulmonary Tuberculosis Using XGBoosting Method

inferred← citeshttps://doi.org/10.1007/978-981-19-3148-2_46openalex-snapshot

Medical Image Classifications: Deep Learning Prospective

inferred← citeshttps://doi.org/10.1007/978-981-19-9228-5_7openalex-snapshot

Diagnosis of Pulmonary Diseases from Chest X-ray Using Deep Learning Approaches

inferred← citeshttps://doi.org/10.1007/978-981-32-9763-0_9openalex-snapshot

New Technologies for TB Control in Migrating Population

inferred← citeshttps://doi.org/10.1007/978-981-33-4909-4_31openalex-snapshot

Tuberculosis Detection from CXR: An Approach Using Transfer Learning with Various CNN Architectures

inferred← citeshttps://doi.org/10.1007/978-981-92-3501-8_30openalex-snapshot

ED-Mask2Former: Dynamic Hierarchical Query and Edge-Guided Enhancement for Boundary-Aware Medical Image Segmentation

inferred← citeshttps://doi.org/10.1007/978-981-95-0036-9_31openalex-snapshot

KD-MedSAM: Lightweight Knowledge Distillation of Segment Anything Model for Multi-modality Medical Image Segmentation

inferred← citeshttps://doi.org/10.1007/978-981-95-4831-6_13openalex-snapshot

Ensemble Learning Inspired Model for Medical Image Segmentation

inferred← citeshttps://doi.org/10.1007/978-981-95-6739-3_18openalex-snapshot

Cascading Features of Convolutional Neural Network for Pneumoconiosis Detection

inferred← citeshttps://doi.org/10.1007/978-981-95-6739-3_23openalex-snapshot

Comparison of Explainability Algorithms for Pneumoconiosis Classification Deep Learning Models

inferred← citeshttps://doi.org/10.1007/978-981-96-0963-5_19openalex-snapshot

TGCM: Cross-Domain Few-Shot Semantic Segmentation via One-Shot Target Guided CutMix

inferred← citeshttps://doi.org/10.1007/978-981-96-2684-7_28openalex-snapshot

A Spatially Enhanced CNN and Multiscale Transformer Fusion Approach for Chest Radiograph Registration

inferred← citeshttps://doi.org/10.1007/978-981-96-6095-7_12openalex-snapshot

Innovative Practices for Detecting Infections in X-Ray Images Using AI-Based Techniques

inferred← citeshttps://doi.org/10.1007/978-981-96-8895-1_26openalex-snapshot

An Ensemble Approach for Tuberculosis Detection Using Bronchoscopy Diagnosis in Convolutional Neural Network and Residual Networks Through Feature Fusion

inferred← citeshttps://doi.org/10.1007/978-981-96-9242-2_32openalex-snapshot

Machine Learning Models for Predicting Type 2 Diabetes Using Ensemble Techniques and Performance Evaluation

inferred← citeshttps://doi.org/10.1007/978-981-96-9533-1_15openalex-snapshot

Shape-Aware Thoracic Edge Map Chest X-Ray Representation for Pulmonary Abnormality Screening

inferred← citeshttps://doi.org/10.1007/978-981-97-7801-0_17openalex-snapshot

Quantum Lung Segmentation: QCU-Net Applied to Chest X-Ray Images

inferred← citeshttps://doi.org/10.1007/978-981-97-9507-9_28openalex-snapshot

From Pixels to Predictions: Medical Image Analysis and Prediction of Brain Tumor and Chest Cancer with Vision Transformers

inferred← citeshttps://doi.org/10.1007/978-981-99-7954-7_22openalex-snapshot

Analyze and Detect Lung Disorders Using Machine Learning Approaches—A Systematic Review

inferred← citeshttps://doi.org/10.1007/978-981-99-8339-1_13openalex-snapshot

Investigations on Artificial Intelligence with Its Application to Diagnosis of Drug-Resistant Pulmonary Tuberculosis

inferred← citeshttps://doi.org/10.1007/s00330-019-06564-3openalex-snapshot

Deep learning: definition and perspectives for thoracic imaging

inferred← citeshttps://doi.org/10.1007/s00330-020-07219-4openalex-snapshot

Deep learning–based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals

inferred← citeshttps://doi.org/10.1007/s00354-024-00255-4openalex-snapshot

Nature-Inspired Algorithms-Based Optimal Features Selection Strategy for COVID-19 Detection Using Medical Images

inferred← citeshttps://doi.org/10.1007/s00371-021-02352-7openalex-snapshot

Segmentation and classification on chest radiography: a systematic survey

inferred← citeshttps://doi.org/10.1007/s00371-025-04070-wopenalex-snapshot

Efficient hierarchical multiscale convolutional attention for accurate medical image segmentation

inferred← citeshttps://doi.org/10.1007/s00500-023-09480-3openalex-snapshot

Diagnosis and multi-classification of lung diseases in CXR images using optimized deep convolutional neural network

inferred← citeshttps://doi.org/10.1007/s00521-021-06134-zopenalex-snapshot

GA-UNet: UNet-based framework for segmentation of 2D and 3D medical images applicable on heterogeneous datasets

inferred← citeshttps://doi.org/10.1007/s00521-021-06243-9openalex-snapshot

A cascaded registration network RCINet with segmentation mask

inferred← citeshttps://doi.org/10.1007/s00521-023-08606-wopenalex-snapshot

BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data

inferred← citeshttps://doi.org/10.1007/s00530-025-01778-6openalex-snapshot

Radlora: a smart low-rank adaptive approach for radiological image classification

inferred← citeshttps://doi.org/10.1007/s10278-021-00440-7openalex-snapshot

2D Statistical Lung Shape Analysis Using Chest Radiographs: Modelling and Segmentation

inferred← citeshttps://doi.org/10.1007/s10278-023-00947-1openalex-snapshot

SAA-SDM: Neural Networks Faster Learned to Segment Organ Images

inferred← citeshttps://doi.org/10.1007/s10278-024-01226-3openalex-snapshot

Construction and Validation of a General Medical Image Dataset for Pretraining

inferred← citeshttps://doi.org/10.1007/s10278-026-02241-2openalex-snapshot

From Redaction to Restoration: Deep Learning for Medical Image Deidentification and Reconstruction

inferred← citeshttps://doi.org/10.1007/s10462-023-10457-9openalex-snapshot

Review on chest pathogies detection systems using deep learning techniques

inferred← citeshttps://doi.org/10.1007/s10479-021-04154-5openalex-snapshot

Depth-wise dense neural network for automatic COVID19 infection detection and diagnosis

inferred← citeshttps://doi.org/10.1007/s10489-020-01829-7openalex-snapshot

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

inferred← citeshttps://doi.org/10.1007/s10489-020-01941-8openalex-snapshot

Differentiation of COVID-19 conditions in planar chest radiographs using optimized convolutional neural networks

inferred← citeshttps://doi.org/10.1007/s10489-022-03659-1openalex-snapshot

Deformable image registration with attention-guided fusion of multi-scale deformation fields

inferred← citeshttps://doi.org/10.1007/s10515-021-00304-yopenalex-snapshot

Gradient contouring and texture modelling based CAD system for improved TB classification

inferred← citeshttps://doi.org/10.1007/s10586-022-03664-6openalex-snapshot

A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images

inferred← citeshttps://doi.org/10.1007/s10598-023-09568-3openalex-snapshot

Hardness Analysis of X-Ray Images for Neural-Network Tuberculosis Diagnosis

inferred← citeshttps://doi.org/10.1007/s10598-023-09587-0openalex-snapshot

Robustness Analysis of Chest X-Ray Computer Tuberculosis Diagnosis

inferred← citeshttps://doi.org/10.1007/s10916-018-0991-9openalex-snapshot

Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs

inferred← citeshttps://doi.org/10.1007/s10916-018-1058-7openalex-snapshot

Inter-Patient Modelling of 2D Lung Variations from Chest X-Ray Imaging via Fourier Descriptors

inferred← citeshttps://doi.org/10.1007/s10916-019-1222-8openalex-snapshot

Analysis of Tuberculosis in Chest Radiographs for Computerized Diagnosis using Bag of Keypoint Features

inferred← citeshttps://doi.org/10.1007/s11042-019-07984-5openalex-snapshot

Automated TB classification using ensemble of deep architectures

inferred← citeshttps://doi.org/10.1007/s11042-022-13843-7openalex-snapshot

CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays

inferred← citeshttps://doi.org/10.1007/s11042-023-15204-4openalex-snapshot

RETRACTED ARTICLE: Smart healthcare system using integrated and lightweight ECC with private blockchain for multimedia medical data processing

inferred← citeshttps://doi.org/10.1007/s11042-023-17194-9openalex-snapshot

HPFL: hyper-network guided personalized federated learning for multi-center tuberculosis chest x-ray diagnosis

inferred← citeshttps://doi.org/10.1007/s11042-023-17220-wopenalex-snapshot

Impact of attention mechanisms for organ segmentation in chest x-ray images over U-Net model

inferred← citeshttps://doi.org/10.1007/s11042-025-20614-7openalex-snapshot

Meta-analysis of quantum convolutional neural networks for automated tuberculosis screening on chest x-rays

inferred← citeshttps://doi.org/10.1007/s11042-025-21094-5openalex-snapshot

LuGSAM: a novel framework for integrating text prompts to Segment Anything Model (SAM) for segmentation tasks of ICU chest x-rays

inferred← citeshttps://doi.org/10.1007/s11227-025-07516-zopenalex-snapshot

Knowledge-driven federated learning: A systematic literature review on approaches, challenges, and prospects

inferred← citeshttps://doi.org/10.1007/s11263-025-02517-0openalex-snapshot

Segment Anything in Context with Vision Foundation Models

inferred← citeshttps://doi.org/10.1007/s11277-020-07075-xopenalex-snapshot

A Novel Ensemble Method for PTB Classification in CXRs

inferred← citeshttps://doi.org/10.1007/s11280-024-01266-3openalex-snapshot

Foundation models matter: federated learning for multi-center tuberculosis diagnosis via adaptive regularization and model-contrastive learning

inferred← citeshttps://doi.org/10.1007/s11517-020-02192-yopenalex-snapshot

Contour-based lung shape analysis in order to tuberculosis detection: modeling and feature description

inferred← citeshttps://doi.org/10.1007/s11517-024-03048-5openalex-snapshot

Direct lingam and visibility graphs for analyzing brain connectivity in BCI

inferred← citeshttps://doi.org/10.1007/s11517-024-03056-5openalex-snapshot

Adapted generative latent diffusion models for accurate pathological analysis in chest X-ray images

inferred← citeshttps://doi.org/10.1007/s11548-015-1242-xopenalex-snapshot

Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays

inferred← citeshttps://doi.org/10.1007/s11548-016-1359-6openalex-snapshot

Edge map analysis in chest X-rays for automatic pulmonary abnormality screening

inferred← citeshttps://doi.org/10.1007/s11548-018-1857-9openalex-snapshot

Detecting drug-resistant tuberculosis in chest radiographs

inferred← citeshttps://doi.org/10.1007/s11548-019-01917-1openalex-snapshot

A review on lung boundary detection in chest X-rays

inferred← citeshttps://doi.org/10.1007/s11548-022-02730-zopenalex-snapshot

Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images

inferred← citeshttps://doi.org/10.1007/s11760-025-04639-xopenalex-snapshot

Enhanced U2-net with residual depthwise separable and split-attention blocks for medical image segmentation

inferred← citeshttps://doi.org/10.1007/s12530-023-09491-3openalex-snapshot

A survey on recent trends in deep learning for nucleus segmentation from histopathology images

inferred← citeshttps://doi.org/10.1007/s12530-023-09516-xopenalex-snapshot

New attention-gated residual deep convolutional network for accurate lung segmentation in chest x-rays

inferred← citeshttps://doi.org/10.1007/s12530-024-09620-6openalex-snapshot

Deep residual learning-based denoiser for medical X-ray images

inferred← citeshttps://doi.org/10.1007/s12530-026-09828-8openalex-snapshot

ABRB-Net: enhancing medical image segmentation with attention based boundary refinement

inferred← citeshttps://doi.org/10.1007/s12559-021-09955-1openalex-snapshot

Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images

inferred← citeshttps://doi.org/10.1007/s12559-023-10198-5openalex-snapshot

Prototype Consistency Learning for Medical Image Segmentation by Cross Pseudo Supervision

inferred← citeshttps://doi.org/10.1007/s12652-019-01281-7openalex-snapshot

Chest X-ray segmentation using Sauvola thresholding and Gaussian derivatives responses

inferred← citeshttps://doi.org/10.1007/s12652-020-02069-wopenalex-snapshot

RETRACTED ARTICLE: Regression Neural Network segmentation approach with LIDC-IDRI for lung lesion

inferred← citeshttps://doi.org/10.1007/s12652-021-03464-7openalex-snapshot

Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques

inferred← citeshttps://doi.org/10.1007/s13042-023-02017-yopenalex-snapshot

Sharp dense U-Net: an enhanced dense U-Net architecture for nucleus segmentation

inferred← citeshttps://doi.org/10.1007/s13246-021-00980-wopenalex-snapshot

Proposing a novel multi-instance learning model for tuberculosis recognition from chest X-ray images based on CNNs, complex networks and stacked ensemble

inferred← citeshttps://doi.org/10.1007/s13246-025-01564-8openalex-snapshot

A novel UNet-SegNet and vision transformer architectures for efficient segmentation and classification in medical imaging

inferred← citeshttps://doi.org/10.1007/s13369-026-11239-5openalex-snapshot

CS-TransED: An Optimized Hybrid CNN-Transformer with Channel-Spatial Attention for Computationally Efficient and High-Accuracy Multimodal Medical Image Segmentation

inferred← citeshttps://doi.org/10.1007/s40846-015-0084-xopenalex-snapshot

Semi-automated Lung Field Segmentation in Scoliosis Radiographs: An Exploratory Study

inferred← citeshttps://doi.org/10.1007/s42600-021-00152-5openalex-snapshot

Segmentation-guided network for automatic thoracic pathology classification

inferred← citeshttps://doi.org/10.1007/s42600-022-00242-yopenalex-snapshot

Covid-19 detection using chest X-rays: is lung segmentation important for generalization?

inferred← citeshttps://doi.org/10.1007/s42979-021-00888-yopenalex-snapshot

Classical–Quantum Transfer Learning for Image Classification

inferred← citeshttps://doi.org/10.1007/s42979-021-00890-4openalex-snapshot

An Analysis of Adaptable Intelligent Models for Pulmonary Tuberculosis Detection and Classification

inferred← citeshttps://doi.org/10.1007/s42979-023-01848-4openalex-snapshot

Vision Transformers for Lung Segmentation on CXR Images

inferred← citeshttps://doi.org/10.1007/s42979-023-02573-8openalex-snapshot

A Systematic Survey of Automatic Detection of Lung Diseases from Chest X-Ray Images: COVID-19, Pneumonia, and Tuberculosis

inferred← citeshttps://doi.org/10.1007/s42979-024-02995-yopenalex-snapshot

An Efficient Approach Based on Attention ConvMixer Model for Lung Segmentation

inferred← citeshttps://doi.org/10.1007/s42979-024-03164-xopenalex-snapshot

A Deep Ensemble Approach for Lung Disease Classification in Chest X-Ray Across Data Distribution Shifts and Unseen Data Generalization

inferred← citeshttps://doi.org/10.1007/s44163-025-00329-2openalex-snapshot

Tuberculosis detection with customized CNN and oversampling techniques: a deep learning approach

inferred← citeshttps://doi.org/10.1007/s44174-024-00244-7openalex-snapshot

Optimized CNN-Based Approach for Tuberculosis Detection from Chest X-rays Under Limited Data Constraints

inferred← citeshttps://doi.org/10.1007/s44196-026-01200-7openalex-snapshot

An Automated Detection and Classification of Lung Diseases Using a Hybrid Deep Learning Technique on Chest X-ray Images

inferred← citeshttps://doi.org/10.1016/b978-0-323-96129-5.00001-9openalex-snapshot

A deep ensemble network for lung segmentation with stochastic weighted averaging

inferred← citeshttps://doi.org/10.1016/j.aej.2022.10.053openalex-snapshot

A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images

inferred← citeshttps://doi.org/10.1016/j.aej.2025.09.065openalex-snapshot

DSSINet: Dynamic token selection and spatial interaction framework for medical image segmentation

inferred← citeshttps://doi.org/10.1016/j.artmed.2019.101744openalex-snapshot

Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection

inferred← citeshttps://doi.org/10.1016/j.asoc.2022.109319openalex-snapshot

Chest X-ray analysis empowered with deep learning: A systematic review

inferred← citeshttps://doi.org/10.1016/j.asoc.2025.114450openalex-snapshot

Contrastive learning strategies for better image classification with imbalanced datasets

inferred← citeshttps://doi.org/10.1016/j.bbe.2021.12.001openalex-snapshot

Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network

inferred← citeshttps://doi.org/10.1016/j.biosx.2025.100617openalex-snapshot

Enhanced cardiovascular diagnostics using wearable ECG and bioimpedance monitoring with LightGBM classifier

inferred← citeshttps://doi.org/10.1016/j.bspc.2021.102959openalex-snapshot

CNL-UNet: A novel lightweight deep learning architecture for multimodal biomedical image segmentation with false output suppression

inferred← citeshttps://doi.org/10.1016/j.bspc.2022.104068openalex-snapshot

Segmentation of lung cancer-caused metastatic lesions in bone scan images using self-defined model with deep supervision

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.104607openalex-snapshot

Automated identification of the preclinical stage of coal workers' pneumoconiosis from digital chest radiography using three-stage cascaded deep learning model

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.104916openalex-snapshot

MCLSG:Multi-modal classification of lung disease and severity grading framework using consolidated feature engineering mechanisms

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105330openalex-snapshot

MMS-Net: Multi-level multi-scale feature extraction network for medical image segmentation

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105536openalex-snapshot

GAN inversion-based semi-supervised learning for medical image segmentation

inferred← citeshttps://doi.org/10.1016/j.bspc.2023.105831openalex-snapshot

Analysis of the Clever Hans effect in COVID-19 detection using Chest X-Ray images and Bayesian Deep Learning

inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106493openalex-snapshot

Rethinking deep active learning for medical image segmentation: A diffusion and angle-based framework

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.107773openalex-snapshot

LiteNeXt: A novel lightweight ConvMixer-based model with Self-embedding Representation Parallel for medical image segmentation

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.108346openalex-snapshot

Tuberculosis detection on chest X-rays using two-dimensional multiscale symbolic dynamic entropy

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110677openalex-snapshot

CM-SAM: Chaos-enhanced hybrid encoder for medical image segmentation with segment anything model

inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110989openalex-snapshot

Explainable AI for drug-resistant tuberculosis detection from chest X-rays: Multi-level interpretability and cross-dataset transfer learning

inferred← citeshttps://doi.org/10.1016/j.cmpb.2021.106058openalex-snapshot

Extreme Learning Machine based Differentiation of Pulmonary Tuberculosis in Chest Radiographs using Integrated Local Feature Descriptors

inferred← citeshttps://doi.org/10.1016/j.cmpb.2022.106947openalex-snapshot

Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification

inferred← citeshttps://doi.org/10.1016/j.cmpb.2022.107262openalex-snapshot

Rapid diagnosis of Covid-19 infections by a progressively growing GAN and CNN optimisation

inferred← citeshttps://doi.org/10.1016/j.cmpbup.2023.100133openalex-snapshot

ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images

inferred← citeshttps://doi.org/10.1016/j.cmpbup.2024.100171openalex-snapshot

A sustainable neuromorphic framework for disease diagnosis using digital medical imaging

inferred← citeshttps://doi.org/10.1016/j.cmpbup.2025.100211openalex-snapshot

Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2021.105002openalex-snapshot

COVID-19 infection localization and severity grading from chest X-ray images

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.105233openalex-snapshot

A review of deep learning-based detection methods for COVID-19

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.106646openalex-snapshot

High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110392openalex-snapshot

Exploring best-performing radiomic features with combined multilevel discrete wavelet decompositions for multiclass COVID-19 classification using chest X-ray images

inferred← citeshttps://doi.org/10.1016/j.compmedimag.2016.04.002openalex-snapshot

Atlas-based rib-bone detection in chest X-rays

inferred← citeshttps://doi.org/10.1016/j.compmedimag.2021.101931openalex-snapshot

Deformable adversarial registration network with multiple loss constraints

inferred← citeshttps://doi.org/10.1016/j.compmedimag.2026.102756openalex-snapshot

DCSM-Unet: Dual-path heterogeneous encoder with content-aware guidance and differential feature learning

inferred← citeshttps://doi.org/10.1016/j.crad.2018.12.015openalex-snapshot

How far have we come? Artificial intelligence for chest radiograph interpretation

inferred← citeshttps://doi.org/10.1016/j.dsp.2024.104975openalex-snapshot

Attention enhanced residual network for automatic pulmonary tuberculosis detection on chest radiographs images

inferred← citeshttps://doi.org/10.1016/j.engappai.2023.106805openalex-snapshot

Density-based one-shot active learning for image segmentation

inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111575openalex-snapshot

Tuberculosis and pneumonia diagnosis in chest X-rays by large adaptive filter and aligning normalized network with report-guided multi-level alignment

showing the first 200 of 667 · page through all 667

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.