mlsanityData to 7 Oct 2026

Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images

arXiv 2401.07020 ↗First posted 13 Jan 2024eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Since 2019, the global dissemination of the Coronavirus and its novel strains has resulted in a surge of new infections. The use of X-ray and computed tomography (CT) imaging techniques is critical in diagnosing and managing COVID-19. Incorporating artificial intelligence (AI) into the field of medical imaging is a powerful combination that can provide valuable support to healthcare professionals.This paper focuses on the methodological approach of using machine learning (ML) to enhance medical imaging for COVID-19 diagnosis.For example, deep learning can accurately distinguish lesions from other parts of the lung without human intervention in a matter of minutes.Moreover, ML can enhance performance efficiency by assisting radiologists in making more precise clinical decisions, such as detecting and distinguishing Covid-19 from different respiratory infections and segmenting infections in CT and X-ray images, even when the lesions have varying sizes and shapes.This article critically assesses machine learning methodologies utilized for the segmentation, classification, and detection of Covid-19 within CT and X-ray images, which are commonly employed tools in clinical and hospital settings to represent the lung in various aspects and extensive detail.There is a widespread expectation that this technology will continue to hold a central position within the healthcare sector, driving further progress in the management of the pandemic.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 51 of its 98 references that are in the map. First posted on arXiv 13 Jan 2024.

Builds on

51 of 98 references.

References · 51

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  5. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  6. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
  7. 2022Generative Adversarial Networkscited by 5,603
  8. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  9. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  10. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  11. 2017Squeeze-and-Excitation Networkscited by 4,763
  12. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
38 more
  1. 2014Going Deeper with Convolutionscited by 4,231
  2. 2015Learning Deep Features for Discriminative Localizationcited by 2,430
  3. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  4. 2017Non-local Neural Networkscited by 2,290
  5. 2016Identity Mappings in Deep Residual Networkscited by 1,960
  6. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  7. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  8. 2016Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learningcited by 1,509
  9. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  10. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  11. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  12. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,054
  13. 2018Dual Attention Network for Scene Segmentationcited by 1,001
  14. 2017Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?cited by 551
  15. 2018A Survey on Deep Transfer Learningcited by 417
  16. 2015Scalable Bayesian Optimization Using Deep Neural Networkscited by 284
  17. 2020COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Imagescited by 264
  18. 2020Can AI help in screening Viral and COVID-19 pneumonia?cited by 200
  19. 2019Domain-Specific Batch Normalization for Unsupervised Domain Adaptationcited by 197
  20. 2020Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Imagescited by 184
  21. 2018Tell Me Where to Look: Guided Attention Inference Networkcited by 168
  22. 2020MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Datacited by 121
  23. 2018Attention Branch Network: Learning of Attention Mechanism for Visual Explanationcited by 108
  24. 2020MosMedData: Chest CT Scans With COVID-19 Related Findings Datasetcited by 103
  25. 2020COVID-CT-Dataset: A CT Scan Dataset about COVID-19cited by 93
  26. 2020Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networkscited by 86
  27. 2020Unpaired Multi-modal Segmentation via Knowledge Distillationcited by 81
  28. 2020CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray imagescited by 68
  29. 2020Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural networkcited by 43
  30. 2020COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Imagescited by 34
  31. 2020Lung Infection Quantification of COVID-19 in CT Images with Deep Learningcited by 28
  32. 2020COVID-19 Infection Map Generation and Detection from Chest X-Ray Imagescited by 28
  33. 2020Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumoniacited by 26
  34. 2020Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classificationcited by 22
  35. 2020A Light CNN for detecting COVID-19 from CT scans of the chestcited by 16
  36. 2020Mapping the Landscape of Artificial Intelligence Applications against COVID-19cited by 14
  37. 2020Towards an Effective and Efficient Deep Learning Model for COVID-19 Patterns Detection in X-ray Imagescited by 13
  38. 2020Multi-Task Driven Explainable Diagnosis of COVID-19 using Chest X-ray Imagescited by 7

50 most cited of 51 All 51 (JSON)

Built on by

Not cited yet.