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A Sustainable Deep Learning-Based Framework for Automated Segmentation of COVID-19 Infected Regions: Using U-Net with an Attention Mechanism and Boundary Loss Function

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What this work says

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

inferredcites →https://doi.org/10.1001/jama.2020.1585openalex-snapshot
Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus–Infected Pneumonia in Wuhan, China
inferredcites →https://doi.org/10.1002/ima.22527openalex-snapshot
Automatic COVID ‐19 CT segmentation using U‐Net integrated spatial and channel attention mechanism
inferredcites →https://doi.org/10.1007/978-3-030-00889-5_1openalex-snapshot
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-030-00934-2_81openalex-snapshot
CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation
inferredcites →https://doi.org/10.1007/978-3-030-59710-8_15openalex-snapshot
DECAPS: Detail-Oriented Capsule Networks
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-66179-7_71openalex-snapshot
Progressive and Multi-path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images
inferredcites →https://doi.org/10.1007/s00330-021-07715-1openalex-snapshot
A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)
inferredcites →https://doi.org/10.1007/s00607-021-00992-0openalex-snapshot
An IoT-enabled smart health care system for screening of COVID-19 with multi layers features fusion and selection
inferredcites →https://doi.org/10.1007/s10044-021-00984-yopenalex-snapshot
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks
inferredcites →https://doi.org/10.1007/s10096-020-03901-zopenalex-snapshot
Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks
inferredcites →https://doi.org/10.1007/s11042-021-10841-zopenalex-snapshot
An improved segmentation algorithm of CT image based on U-Net network and attention mechanism
inferredcites →https://doi.org/10.1007/s11042-021-11153-yopenalex-snapshot
Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning
inferredcites →https://doi.org/10.1007/s11554-021-01103-0openalex-snapshot
An IoT-enabled real-time overhead view person detection system based on Cascade-RCNN and transfer learning
inferredcites →https://doi.org/10.1007/s13246-020-00865-4openalex-snapshot
Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks
inferredcites →https://doi.org/10.1007/s42979-021-00823-1openalex-snapshot
A Comprehensive Survey of COVID-19 Detection Using Medical Images
inferredcites →https://doi.org/10.1016/j.cmpb.2020.105532openalex-snapshot
COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios
inferredcites →https://doi.org/10.1016/j.imu.2021.100681openalex-snapshot
Robust chest CT image segmentation of COVID-19 lung infection based on limited data
inferredcites →https://doi.org/10.1016/j.inffus.2021.02.013openalex-snapshot
COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images
inferredcites →https://doi.org/10.1016/j.irbm.2020.05.003openalex-snapshot
Deep Transfer Learning Based Classification Model for COVID-19 Disease
inferredcites →https://doi.org/10.1016/j.media.2019.01.012openalex-snapshot
Attention gated networks: Learning to leverage salient regions in medical images
inferredcites →https://doi.org/10.1016/j.media.2020.101851openalex-snapshot
Boundary loss for highly unbalanced segmentation
inferredcites →https://doi.org/10.1016/j.patcog.2020.107700openalex-snapshot
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients
inferredcites →https://doi.org/10.1016/j.procs.2018.01.104openalex-snapshot
Lung CT Image Segmentation Using Deep Neural Networks
inferredcites →https://doi.org/10.1016/j.scs.2020.102571openalex-snapshot
A deep learning-based social distance monitoring framework for COVID-19
inferredcites →https://doi.org/10.1016/j.scs.2021.102777openalex-snapshot
Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic
inferredcites →https://doi.org/10.1016/j.scs.2021.102908openalex-snapshot
Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies
inferredcites →https://doi.org/10.1038/s41592-019-0686-2openalex-snapshot
SciPy 1.0: fundamental algorithms for scientific computing in Python
inferredcites →https://doi.org/10.1038/s41598-020-76550-zopenalex-snapshot
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images
inferredcites →https://doi.org/10.1056/nejmoa2001017openalex-snapshot
A Novel Coronavirus from Patients with Pneumonia in China, 2019
inferredcites →https://doi.org/10.1109/access.2020.3011406openalex-snapshot
Comparison of Deep-Learning-Based Segmentation Models: Using Top View Person Images
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/jiot.2019.2951365openalex-snapshot
Exploring Deep Learning Models for Overhead View Multiple Object Detection
inferredcites →https://doi.org/10.1109/jiot.2020.3034074openalex-snapshot
An IoT-Based Deep Learning Framework for Early Assessment of Covid-19
inferredcites →https://doi.org/10.1109/jiot.2021.3052067openalex-snapshot
A Deep-Learning-Based Smart Healthcare System for Patient’s Discomfort Detection at the Edge of Internet of Things
inferredcites →https://doi.org/10.1109/mnet.011.2000458openalex-snapshot
Explainable AI and Mass Surveillance System-Based Healthcare Framework to Combat COVID-I9 Like Pandemics
inferredcites →https://doi.org/10.1109/rbme.2020.2987975openalex-snapshot
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19
inferredcites →https://doi.org/10.1109/tcbb.2021.3065361openalex-snapshot
Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images
inferredcites →https://doi.org/10.1109/tip.2021.3058783openalex-snapshot
JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation
inferredcites →https://doi.org/10.1109/tmi.2018.2857800openalex-snapshot
Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images
inferredcites →https://doi.org/10.1109/tmi.2020.2995965openalex-snapshot
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
inferredcites →https://doi.org/10.1109/tmi.2020.2996645openalex-snapshot
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
inferredcites →https://doi.org/10.1136/bmj.m1328openalex-snapshot
Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal
inferredcites →https://doi.org/10.1148/ryct.2020200034openalex-snapshot
Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review
inferredcites →https://doi.org/10.1155/2022/5303651openalex-snapshot
APU‐Net: An Attention Mechanism Parallel U‐Net for Lung Tumor Segmentation
inferredcites →https://doi.org/10.1177/1550147720934738openalex-snapshot
Convolutional neural network–based person tracking using overhead views
inferredcites →https://doi.org/10.1186/s12911-021-01649-wopenalex-snapshot
U-Net combined with multi-scale attention mechanism for liver segmentation in CT images
inferredcites →https://doi.org/10.1609/aaai.v35i6.16617openalex-snapshot
MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
inferredcites →https://doi.org/10.2214/ajr.20.22954openalex-snapshot
Coronavirus Disease 2019 (COVID-19): Role of Chest CT in Diagnosis and Management
inferredcites →https://doi.org/10.2214/ajr.20.23034openalex-snapshot
Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients
inferredcites →https://doi.org/10.3390/electronics11010130openalex-snapshot
SD-UNet: A Novel Segmentation Framework for CT Images of Lung Infections

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.4777244openalex-extracted
Diep-Unet: Dual-Path Information Enhanced Pyramid Unet for Covid-19 Lung Infection Segmentation
inferred← citeshttps://doi.org/10.2139/ssrn.4779254openalex-extracted
Titania: Mapping Forest Tree Dieback with Self-Distilling U-Net on Sentinel-2 Images
inferred← citeshttps://doi.org/10.1007/978-3-031-78198-8_5openalex-snapshot
A Fast Domain-Inspired Unsupervised Method to Compute COVID-19 Severity Scores from Lung CT
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inferred← citeshttps://doi.org/10.1007/s11517-022-02758-yopenalex-snapshot
A survey of machine learning-based methods for COVID-19 medical image analysis
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109977openalex-snapshot
Dual-path information enhanced pyramid Unet for COVID-19 lung infection segmentation
inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103527openalex-snapshot
From data to diagnosis: AI-driven multi-modal fusion and generative AI-enhanced GAN-based MRI for brain tumour detection
inferred← citeshttps://doi.org/10.1016/j.ipemt.2022.100008openalex-snapshot
Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging
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Deep learning-based automatic segmentation of COVID-19 in chest X-ray images using ensemble neural net sentinel algorithm
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inferred← citeshttps://doi.org/10.1038/s41598-025-26090-1openalex-snapshot
Automated segmentation of COVID-19 lesions in CT scans using attention U-net with hybrid loss functions
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A review of dynamic multi-scale feature integration and edge enhancement in medical image segmentation
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Medical Internet of Things and Deep Convolutional Neural Network for Classification of Chest X-rays Images
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Medical Internet of Things for Classification of Pathological ECG Beats Based on Fractional Fourier Transform and Hyperparameter Tuning
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Artificial Intelligence and Blockchain Enabled Smart Healthcare System for Monitoring and Detection of COVID-19 in Biomedical Images
inferred← citeshttps://doi.org/10.1109/tits.2022.3210490openalex-snapshot
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Sustainable Flood Risk Identification and Assessment Using Artificial Intelligence: A Literature Review and Case Scenario
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Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model
inferred← citeshttps://doi.org/10.1371/journal.pone.0278487openalex-snapshot
Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust
inferred← citeshttps://doi.org/10.14569/ijacsa.2024.0150594openalex-snapshot
Enhanced U-Net Architecture for Lung Segmentation on Computed Tomography and X-Ray Images
inferred← citeshttps://doi.org/10.32604/cmc.2023.038059openalex-snapshot
A Systematic Literature Review of Deep Learning Algorithms for Segmentation of the COVID-19 Infection
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Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
inferred← citeshttps://doi.org/10.3390/diagnostics13091658openalex-snapshot
SAA-UNet: Spatial Attention and Attention Gate UNet for COVID-19 Pneumonia Segmentation from Computed Tomography
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Rapid and Accurate Diagnosis of COVID-19 Cases from Chest X-ray Images through an Optimized Features Extraction Approach
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Technological Advancements and Elucidation Gadgets for Healthcare Applications: An Exhaustive Methodological Review-Part-II (Robotics, Drones, 3D-Printing, Internet of Things, Virtual/Augmented and Mixed Reality)
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Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models
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