{"title":"基于计算机断层扫描图像的Covid-19分类的自监督对比学习","authors":"K. Mohit, Rajeev Gupta, B. Kumar","doi":"10.1109/UPCON56432.2022.9986406","DOIUrl":null,"url":null,"abstract":"Computer-aided diagnosis (CAD) emerges as an exhaustive diagnostic tool in the Covid-19 pandemic outbreak and is enormously investigated for automatic and more accurate detections. Artificial intelligence (AI) based radiographic images (Computed Tomography, X-Ray, Lung Ultrasound) interpretation improves the overall diagnosis efficiency of Covid-19 infections. In this paper, CAD based deep meta learning approach has been discussed for automatically quick analysis of chest computed tomography (CT) images regarding the early detection of corona virus (Covid-19) presence inside a subject. We incorporated a self-supervised contrastive-learning neural network for unbiased feature representation and classifications using fine-tuned pre-trained Inception module on 28203 chest CT images. This trainable multi-shot end-to-end deep learning architecture is validated on public dataset of normal and covid-19 CT images obtaining normalized accuracy of 0.9708. Results verify our model to be able enough to assist radiologists and specialists in screening and correct diagnosis of Covid-19 patients in less span of time.","PeriodicalId":185782,"journal":{"name":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Self-Supervised Contrastive Learning for Covid-19 Classification from Computed Tomography Images\",\"authors\":\"K. Mohit, Rajeev Gupta, B. Kumar\",\"doi\":\"10.1109/UPCON56432.2022.9986406\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Computer-aided diagnosis (CAD) emerges as an exhaustive diagnostic tool in the Covid-19 pandemic outbreak and is enormously investigated for automatic and more accurate detections. Artificial intelligence (AI) based radiographic images (Computed Tomography, X-Ray, Lung Ultrasound) interpretation improves the overall diagnosis efficiency of Covid-19 infections. In this paper, CAD based deep meta learning approach has been discussed for automatically quick analysis of chest computed tomography (CT) images regarding the early detection of corona virus (Covid-19) presence inside a subject. We incorporated a self-supervised contrastive-learning neural network for unbiased feature representation and classifications using fine-tuned pre-trained Inception module on 28203 chest CT images. This trainable multi-shot end-to-end deep learning architecture is validated on public dataset of normal and covid-19 CT images obtaining normalized accuracy of 0.9708. Results verify our model to be able enough to assist radiologists and specialists in screening and correct diagnosis of Covid-19 patients in less span of time.\",\"PeriodicalId\":185782,\"journal\":{\"name\":\"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UPCON56432.2022.9986406\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UPCON56432.2022.9986406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Self-Supervised Contrastive Learning for Covid-19 Classification from Computed Tomography Images
Computer-aided diagnosis (CAD) emerges as an exhaustive diagnostic tool in the Covid-19 pandemic outbreak and is enormously investigated for automatic and more accurate detections. Artificial intelligence (AI) based radiographic images (Computed Tomography, X-Ray, Lung Ultrasound) interpretation improves the overall diagnosis efficiency of Covid-19 infections. In this paper, CAD based deep meta learning approach has been discussed for automatically quick analysis of chest computed tomography (CT) images regarding the early detection of corona virus (Covid-19) presence inside a subject. We incorporated a self-supervised contrastive-learning neural network for unbiased feature representation and classifications using fine-tuned pre-trained Inception module on 28203 chest CT images. This trainable multi-shot end-to-end deep learning architecture is validated on public dataset of normal and covid-19 CT images obtaining normalized accuracy of 0.9708. Results verify our model to be able enough to assist radiologists and specialists in screening and correct diagnosis of Covid-19 patients in less span of time.