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A Residual-Learning-Based Multi-Scale Parallel-Convolutions- Assisted Efficient CAD System for Liver Tumor Detection

https://doi.org/10.3390/math9101133
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-46723-8_48openalex-snapshot
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
inferredcites →https://doi.org/10.1007/978-981-15-5199-4_17openalex-snapshot
Performance Analysis of Different 2D and 3D CNN Model for Liver Semantic Segmentation: A Review
inferredcites →https://doi.org/10.1007/s11548-018-1801-zopenalex-snapshot
An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning
inferredcites →https://doi.org/10.1016/j.cmpb.2020.105395openalex-snapshot
DENSE-INception U-net for medical image segmentation
inferredcites →https://doi.org/10.1016/j.eswa.2012.02.095openalex-snapshot
A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images
inferredcites →https://doi.org/10.1016/j.eswa.2019.01.055openalex-snapshot
Liver CT sequence segmentation based with improved U-Net and graph cut
inferredcites →https://doi.org/10.1016/j.mehy.2019.109431openalex-snapshot
Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation
inferredcites →https://doi.org/10.1038/s41598-018-33860-7openalex-snapshot
Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing
inferredcites →https://doi.org/10.1080/08998280.2008.11928410openalex-snapshot
Hepatocellular Carcinoma: Management of an Increasingly Common Problem
inferredcites →https://doi.org/10.1109/access.2019.2923218openalex-snapshot
Liver Tumor Segmentation Based on Multi-Scale Candidate Generation and Fractal Residual Network
inferredcites →https://doi.org/10.1109/access.2019.2924584openalex-snapshot
Socio-Technological Factors Affecting User’s Adoption of eHealth Functionalities: A Case Study of China and Ukraine eHealth Systems
inferredcites →https://doi.org/10.1109/access.2019.2929270openalex-snapshot
Liver Semantic Segmentation Algorithm Based on Improved Deep Adversarial Networks in Combination of Weighted Loss Function on Abdominal CT Images
inferredcites →https://doi.org/10.1109/access.2019.2932786openalex-snapshot
A Data Augmentation-Based Framework to Handle Class Imbalance Problem for Alzheimer’s Stage Detection
inferredcites →https://doi.org/10.1109/access.2020.3010794openalex-snapshot
A New Chaotic Map With Dynamic Analysis and Encryption Application in Internet of Health Things
inferredcites →https://doi.org/10.1109/access.2020.3047861openalex-snapshot
A Multiple Layer U-Net, U n -Net, for Liver and Liver Tumor Segmentation in CT
inferredcites →https://doi.org/10.1109/cits.2017.8035318openalex-snapshot
Texture-based treatment prediction by automatic liver tumor segmentation on computed tomography
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/iccicct.2014.6993061openalex-snapshot
Survey on recent CAD system for liver disease diagnosis
inferredcites →https://doi.org/10.1109/icfhr.2016.0054openalex-snapshot
Convolutional Multi-directional Recurrent Network for Offline Handwritten Text Recognition
inferredcites →https://doi.org/10.1109/jbhi.2018.2886276openalex-snapshot
Extending 2-D Convolutional Neural Networks to 3-D for Advancing Deep Learning Cancer Classification With Application to MRI Liver Tumor Differentiation
inferredcites →https://doi.org/10.1109/jbhi.2020.3016306openalex-snapshot
Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images
inferredcites →https://doi.org/10.1109/lsp.2018.2817612openalex-snapshot
Information-Compensated Downsampling for Image Super-Resolution
inferredcites →https://doi.org/10.1109/sampta45681.2019.9030909openalex-snapshot
A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation
inferredcites →https://doi.org/10.1109/tmi.2018.2845918openalex-snapshot
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
inferredcites →https://doi.org/10.1109/tmi.2019.2948320openalex-snapshot
Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images
inferredcites →https://doi.org/10.17179/excli2016-402openalex-snapshot
Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring
inferredcites →https://doi.org/10.24963/ijcai.2019/593openalex-snapshot
Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation
inferredcites →https://doi.org/10.32604/cmc.2021.013966openalex-snapshot
An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection
inferredcites →https://doi.org/10.32604/cmc.2021.014604openalex-snapshot
Medical Diagnosis Using Machine Learning: A Statistical Review
inferredcites →https://doi.org/10.32604/cmc.2021.014943openalex-snapshot
A Comprehensive Review on Medical Diagnosis Using Machine Learning
inferredcites →https://doi.org/10.4236/jcc.2015.311023openalex-snapshot
Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.4304/jait.2.1.63-70openalex-snapshot
Automatic Segmentation of Liver and Tumor for CAD of Liver
inferredcites →https://doi.org/10.48550/arxiv.1702.05970openalex-snapshot
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
inferredcites →https://doi.org/10.54294/25etaxopenalex-snapshot
A semi-automated method for liver tumor segmentation based on 2D region growing with
inferredcites →https://doi.org/10.54294/msg94uopenalex-snapshot
Segmentation of Liver Metastases in CT Scans by Adaptive Thresholding and Morphological Processing

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-3-032-00232-7_61openalex-snapshot
Development of Hybrid Segmentation Technique for Liver Tumor Detection and Extraction Using Neutrosophic Sets and Gaussian Mixture Model
inferred← citeshttps://doi.org/10.1007/s00779-021-01637-xopenalex-snapshot
An IOMT assisted lung nodule segmentation using enhanced receptive field-based modified UNet
inferred← citeshttps://doi.org/10.1007/s11227-021-04007-9openalex-snapshot
A novel two-stream structure for video anomaly detection in smart city management
inferred← citeshttps://doi.org/10.1007/s11227-022-04611-3openalex-snapshot
Exploiting vulnerability of convolutional neural network-based gait recognition system
inferred← citeshttps://doi.org/10.1007/s11265-022-01742-xopenalex-snapshot
A Meta-Heuristic Optimization Based Less Imperceptible Adversarial Attack on Gait Based Surveillance Systems
inferred← citeshttps://doi.org/10.1007/s11704-022-2050-4openalex-snapshot
An efficient deep learning-assisted person re-identification solution for intelligent video surveillance in smart cities
inferred← citeshttps://doi.org/10.1007/s44174-025-00341-1openalex-snapshot
A Systematic Review on Machine Learning Techniques for Liver Disease Detection
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106501openalex-snapshot
Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and Res-UNet++
inferred← citeshttps://doi.org/10.1016/j.gande.2025.08.003openalex-snapshot
Enhanced liver cancer detection: Leveraging pre-processing techniques for 3D CT volumes
inferred← citeshttps://doi.org/10.1016/j.ins.2025.121966openalex-snapshot
Automatic liver tumor segmentation of CT and MRI volumes using ensemble ResUNet-InceptionV4 model
inferred← citeshttps://doi.org/10.1016/j.isatra.2022.10.030openalex-snapshot
Panoptic blind image inpainting
inferred← citeshttps://doi.org/10.1016/j.isatra.2022.11.016openalex-snapshot
An autonomous decision-making framework for gait recognition systems against adversarial attack using reinforcement learning
inferred← citeshttps://doi.org/10.1016/j.liver.2024.100251openalex-snapshot
Deep learning technique for automatic liver and liver tumor segmentation in CT images
inferred← citeshttps://doi.org/10.1016/j.measurement.2022.111310openalex-snapshot
Image feature selection based on orthogonal ℓ2,0 norms
inferred← citeshttps://doi.org/10.1016/j.nexres.2025.100465openalex-snapshot
Improving liver cancer diagnosis: A multifaceted approach to automated liver tumor identification in ultrasound scans
inferred← citeshttps://doi.org/10.1109/icetas59148.2023.10346515openalex-snapshot
Deep Learning Approach for Bone Marrow Cell Detection and Classification on Whole-Slide Images
inferred← citeshttps://doi.org/10.1109/jbhi.2023.3278079openalex-snapshot
AC-E Network: Attentive Context-Enhanced Network for Liver Segmentation
inferred← citeshttps://doi.org/10.1155/2021/3110416openalex-snapshot
A Smart Surveillance System for Uncooperative Gait Recognition Using Cycle Consistent Generative Adversarial Networks (CCGANs)
inferred← citeshttps://doi.org/10.1155/2022/2801227openalex-snapshot
A Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Samples Using Squeeze and Excitation Learning
inferred← citeshttps://doi.org/10.1155/2022/4646733openalex-snapshot
An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning
inferred← citeshttps://doi.org/10.1186/s43055-026-01730-2openalex-snapshot
Integrating swin transformer and attention scaling into residual U-net for accurate liver tumor segmentation in medical imaging
inferred← citeshttps://doi.org/10.17816/dd515814openalex-snapshot
Prospects of using computer vision technology to detect urinary stones and liver and kidney neoplasms on computed tomography images of the abdomen and retroperitoneal space
inferred← citeshttps://doi.org/10.32604/cmc.2022.017865openalex-snapshot
An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic
inferred← citeshttps://doi.org/10.32604/iasc.2023.028262openalex-snapshot
An Efficient Method for Underwater Video Summarization and Object Detection Using YoLoV3
inferred← citeshttps://doi.org/10.3390/app14125144openalex-snapshot
Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques
inferred← citeshttps://doi.org/10.3390/bioengineering9080368openalex-snapshot
A Deep Learning Approach for Liver and Tumor Segmentation in CT Images Using ResUNet
inferred← citeshttps://doi.org/10.3390/systems10020024openalex-snapshot
Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment
inferred← citeshttps://doi.org/10.36548/jaicn.2024.1.006openalex-snapshot
ResNet50-Boosted UNet for Improved Liver Segmentation Accuracy
inferred← citeshttps://doi.org/10.4015/s1016237225500279openalex-snapshot
A BIBLIOMETRIC REVIEW OF LIVER TUMOR SEGMENTATION AND CLASSIFICATION TECHNIQUES WITH MEDICAL IMAGING DATASETS AND FUTURE ORIENTATIONS
inferred← citeshttps://doi.org/10.4018/jdm.318415openalex-snapshot
Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks
inferred← citeshttps://doi.org/10.56578/ataiml020301openalex-snapshot
Advanced Hybrid Segmentation Model Leveraging AlexNet Architecture for Enhanced Liver Cancer Detection
inferred← citeshttps://doi.org/10.56726/irjmets38621openalex-snapshot
LIVER TUMOR DETECTION USING IMAGE PROCESSING TECHNIQUE: A SURVEY
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