Deep-Learning-Assisted Highly-Accurate COVID-19 Diagnosis on Lung Computed Tomography Images

• URL: http://arxiv.org/abs/2507.04252v1
• Date: Sun, 06 Jul 2025 05:54:44 GMT
• Title: Deep-Learning-Assisted Highly-Accurate COVID-19 Diagnosis on Lung Computed Tomography Images
• Authors: Yinuo Wang, Juhyun Bae, Ka Ho Chow, Shenyang Chen, Shreyash Gupta,
• Abstract summary: COVID-19 is a severe and acute viral disease that can cause symptoms consistent with pneumonia.<n>The diagnosis of COVID using CT scans has been effective in assisting with RT-PCR diagnosis and severity classifications.<n>In this paper, we proposed a new data quality control pipeline to refine the quality of CT images based on GAN and sliding windows.
• Score: 3.795837769531959
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: COVID-19 is a severe and acute viral disease that can cause symptoms consistent with pneumonia in which inflammation is caused in the alveolous regions of the lungs leading to a build-up of fluid and breathing difficulties. Thus, the diagnosis of COVID using CT scans has been effective in assisting with RT-PCR diagnosis and severity classifications. In this paper, we proposed a new data quality control pipeline to refine the quality of CT images based on GAN and sliding windows. Also, we use class-sensitive cost functions including Label Distribution Aware Loss(LDAM Loss) and Class-balanced(CB) Loss to solve the long-tail problem existing in datasets. Our model reaches more than 0.983 MCC in the benchmark test dataset.

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