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ConvOS:An Interpretable Diagnostic Model for COVID-19 X-ray Imaging |
JIAN Xian-zhong,YOU Guo-da,ZHANG Tao,ZHANG Zhen-wen |
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200090, China |
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Abstract In order to improve the diagnostic accuracy of deep learning model in X-ray diagnosis of COVID-19 and improve the models interpretability, a novel visual diagnosis (ConvOS) model was designed by combining bilateral perturbation comprehensive Gradient significance graph optimization (IGOS++) algorithm and improved ConvNeXt network model. An efficient channel attention (ECA) module was introduced into the residual block of ConvNeXt network model, and IGOS++ was used to analyze the output characteristics of the improved ConvNeXt network model. The optimal insertion loss hyperparameters were obtained, and high-precision saliency maps were generated to improve the performance of visual interpretation and diagnosis of COVID-19 X-ray images. The experimental results on COVIDx dataset show that the accuracy, recall, accuracy and F1-score of ConvOS model reach 93.7%, 92.6%, 96.2% and 94.4%, respectively. Compared with other diagnostic models, the classification performance index is better, the region of interest (ROI) of significance graph is more accurate, and the diagnostic confidence of the model is higher.
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Received: 21 July 2022
Published: 22 August 2023
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[1] |
国家卫生健康委办公厅. 关于印发新型冠状病毒肺炎诊疗方案(试行第八版修订版)的通知[EB]. http: //www. gov. cn/zhengce/zhengceku/2021-04/15/content_5599795. htm, 2021-04-14.
|
[5] |
郭保苏, 庄集超, 吴凤和, 等. 基于CT图像卷积神经网络处理的新冠肺炎检测[J]. 计量学报, 2021, 42(4): 537-544.
|
[2] |
Ozturk T, Talo M, Yildirim E A, et al. Automated detection of COVID-19 cases using deep neural networks with X-ray images[J]. Computers in biology and medicine, 2020, 121(103792): 510-522.
|
[4] |
Monshi M M A, Poon J, Chung V, et al.
|
|
Guo B S, Zhuang J C, Wu F H, et al. Detection of Novel Coronavirus Disease 2019 Based on Convolutional Neural Network Processing of CT Images [J]. Acta Metrologica Sinica, 2021, 42(4): 537-544.
|
[7] |
Wang L, Lin Z Q, Wong A. Covid-net: A Tailored Deep Convolutional Neural Network Design for Detection of Covid-19 Cases from Chest X-ray Images[J]. Scientific Reports, 2020, 10(1): 1-12.
|
[8] |
Oh Y, Park S, Ye J C. Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets[J]. IEEE transactions on medical imaging, 2020, 39(8): 2688-2700.
|
[10] |
DeGrave A J, Janizek J D, Lee S I. AI for Radiographic COVID-19 Detection Selects Shortcuts Over Signal[J]. Nature Machine Intelligence, 2021, 3(7): 610-619.
|
[12] |
Liu Z, Mao H, Wu C Y, et al. A Convnet for the 2020s[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA. 2022: 11976-11986.
|
[14] |
Wang Q, Wu B, Zhu P, et al. Supplementary Material for ‘ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, Seattle, USA, 2020: 13-19.
|
[15] |
Sundararajan M, Taly A, Yan Q. Axiomatic Attribution for Deep Networks[C]//International Conference on Machine Learning. Toulon, France, 2017: 3319-3328.
|
|
CovidXrayNet: Optimizing Data Augmentation and CNN Hyperparameters for Improved COVID-19 Detection from CXR[J]. Computers in Biology and Medicine, 133(104375): 48-61.
|
[13] |
Hu J, Shen L, Sun G. Squeeze-and-excitation Networks [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA, 2018: 7132-7141.
|
[18] |
He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA. 2016: 770-778.
|
[3] |
Zhang R, Xin T, Qi Z, et al. Diagnosis of Coronavirus Disease 2019 Pneumonia by Using Chest Radiography: Value of Artificial Intelligence[J]. Radiological Society of North America, 2021(2): 88-98.
|
[11] |
Khorram S, Lawson T, Fuxin L. iGOS++ Integrated Gradient Optimized Saliency by Bilateral Perturbations[C] //Proceedings of the Conference on Health, Inference, and Learning. Virtual Event, USA . 2021: 174-182.
|
[6] |
Brunese L, Mercaldo F, Reginelli A, et al. Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays[J]. Computer Methods and Programs in Biomedicine, 2020, 196(105608): 62-73.
|
[9] |
Karim M, Dhmen T, Rebholz-Schuhmann D, et al. DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on chest X-ray Images[EB]. arXiv preprint arXiv: 2004. 04582, 2020.
|
[16] |
Fong R C, Vedaldi A. Interpretable Explanations of Black Boxes by Meaningful Perturbation[C]//Proceedings of the IEEE International Conference on Computer Vision. Honolulu, USA, 2017: 3429-3437.
|
[19] |
Apostolopoulos I D, Mpesiana T A. Covid-19: Automatic Detection from X-Ray Images Utilizing Transfer Learning with Convolutional Neural Networks[J]. Physical and engineering sciences in medicine, 2020, 43(2): 635-640.
|
[17] |
Selvaraju R R, Cogswell M, Das A, et al. Grad-cam: Visual Explanations from Deep Networks Via Gradient-based Localization [C]//Proceedings of the IEEE International Conference on Computer Vision. Honolulu, USA, 2017: 618-626.
|
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