CT-xCOV: a CT-scan based Explainable Framework for COVid-19 diagnosis

• URL: http://arxiv.org/abs/2311.14462v1
• Date: Fri, 24 Nov 2023 13:14:10 GMT
• Title: CT-xCOV: a CT-scan based Explainable Framework for COVid-19 diagnosis
• Authors: Ismail Elbouknify, Afaf Bouhoute, Khalid Fardousse, Ismail Berrada, Abdelmajid Badri
• Abstract summary: CT-xCOV is an explainable framework for COVID-19 diagnosis using Deep Learning (DL) on CT-scans. For lung segmentation, we used the well-known U-Net model. For COVID-19 detection, we compared three different CNN architectures. For visual explanations, we applied three different XAI techniques, namely, Grad-Cam, Integrated Gradient (IG) and LIME.
• Score: 6.2997667081978825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, CT-xCOV, an explainable framework for COVID-19 diagnosis using Deep Learning (DL) on CT-scans is developed. CT-xCOV adopts an end-to-end approach from lung segmentation to COVID-19 detection and explanations of the detection model's prediction. For lung segmentation, we used the well-known U-Net model. For COVID-19 detection, we compared three different CNN architectures: a standard CNN, ResNet50, and DenseNet121. After the detection, visual and textual explanations are provided. For visual explanations, we applied three different XAI techniques, namely, Grad-Cam, Integrated Gradient (IG), and LIME. Textual explanations are added by computing the percentage of infection by lungs. To assess the performance of the used XAI techniques, we propose a ground-truth-based evaluation method, measuring the similarity between the visualization outputs and the ground-truth infections. The performed experiments show that the applied DL models achieved good results. The U-Net segmentation model achieved a high Dice coefficient (98%). The performance of our proposed classification model (standard CNN) was validated using 5-fold cross-validation (acc of 98.40% and f1-score 98.23%). Lastly, the results of the comparison of XAI techniques show that Grad-Cam gives the best explanations compared to LIME and IG, by achieving a Dice coefficient of 55%, on COVID-19 positive scans, compared to 29% and 24% obtained by IG and LIME respectively. The code and the dataset used in this paper are available in the GitHub repository [1].

Related papers

• Enhancing COVID-19 Severity Analysis through Ensemble Methods [13.792760290422185]
  This paper presents a domain knowledge-based pipeline for extracting regions of infection in COVID-19 patients. The severity of the infection is then classified into different categories using an ensemble of three machine-learning models. The proposed system was evaluated on a validation dataset in the AI-Enabled Medical Image Analysis Workshop and COVID-19 Diagnosis Competition.
  arXiv  Detail & Related papers  (2023-03-13T13:59:47Z)
• COVID-19 Detection on Chest X-Ray Images: A comparison of CNN architectures and ensembles [0.0]
  Convolutional neural networks (CNNs) are often used for automatic image classification and they can be very useful in CXR diagnostics. In this paper, 21 different CNN architectures are tested and compared in the task of identifying COVID-19 in CXR images. The best individual CNN instance results were achieved by DenseNet169, with an accuracy of 98.15% and an F1 score of 98.12%.
  arXiv  Detail & Related papers  (2021-11-18T23:28:21Z)
• CNN Filter Learning from Drawn Markers for the Detection of Suggestive Signs of COVID-19 in CT Images [58.720142291102135]
  We propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN) For a few CT images, the user draws markers at representative normal and abnormal regions. The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones.
  arXiv  Detail & Related papers  (2021-11-16T15:03:42Z)
• COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning [70.92379567261304]
  We introduce COVID-Net CT-2, enhanced deep neural networks for COVID-19 detection from chest CT images. We leverage explainability to investigate the decision-making behaviour of COVID-Net CT-2. Results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment.
  arXiv  Detail & Related papers  (2021-01-19T03:04:09Z)
• Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans [72.04652116817238]
  We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification. We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
  arXiv  Detail & Related papers  (2021-01-14T03:45:01Z)
• Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning [0.07994291858620557]
  We propose a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conduct extensive sets of experiments on two CT image datasets, namely the SARS-CoV-2 CT-scan and the COVID19-CT. The obtained results show superior performances for our models compared with previous studies.
  arXiv  Detail & Related papers  (2020-11-09T17:37:31Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans [11.024688703207627]
  This paper presents an extensive comparison of Deep Learning models for lung and COVID-19 lesion segmentation in Computerized Tomography (CT) scans. Four DL architectures (Unet, Linknet, FPN, PSPNet) are combined with 25 randomly and pretrained encoders to construct 200 tested models. Three experimental setups are conducted for lung segmentation, lesion segmentation and lesion segmentation using the original lung masks.
  arXiv  Detail & Related papers  (2020-09-10T08:05:06Z)
• Automated Chest CT Image Segmentation of COVID-19 Lung Infection based on 3D U-Net [0.0]
  The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. We propose an innovative automated segmentation pipeline for COVID-19 infected regions. Our method focuses on on-the-fly generation of unique and random image patches for training by performing several preprocessing methods.
  arXiv  Detail & Related papers  (2020-06-24T17:29:26Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT [49.09507792800059]
  We propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. We evaluate our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP)
  arXiv  Detail & Related papers  (2020-05-07T06:00:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.