CoroPy: A Deep Learning Based Comparison Between X-Ray and CT Scan Images in Covid-19 Detection and Classification

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外文摘要：Coronavirus 2019 (in short， Covid-19)， was first found in the Wuhan province of China in December 2019 and has been declared a global pandemic by WHO in March 2020。 Since its inception， its rapid spread among nations had initially collapsed the world economy and the increasing death-pool created a strong fear among people as the virus spread through human contact。 Initially doctors struggled to diagnose the increasing number of patients as there was less availability of testing kits and failed to treat people efficiently which ultimately led to the collapse of the health sector of several countries。 To help doctors primarily diagnose the virus， researchers around the world have come up with some radiology imaging techniques using the Convolutional Neural Network (CNN)。 While some of them worked on X-ray images and some others on CT scan images， very few of them worked on both the image types where their works are limited to detecting only covid and normal cases and none of them performed any comparative analysis between the performance of these two image types as far as our knowledge goes。 This， therefore， has insisted us to perform a comparative analysis between X-ray and CT scan images。 Thus we came up with a novel CNN model named CoroPy which works for both the image types and can detect normal， Covid-19 and viral pneumonia with great accuracy and shows that X-ray images have overall better performance。

外文关键词：

Covid-19Machine learningDeep learningConvolutional neural networkX-rayCT scanCoroPy

作者：

Jamilur Rahman、Ahashan Habib Niloy、Shammi Akhter Shiba、S. M. Farah Al Fahim、Faizun Nahar Faria、Emtiaz Hussain、Mohammad Zavid Parvez

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作者单位：

BRAC University, Dhaka, Bangladesh

Engineering Institute of Technology, Melbourne, Australia

会议名称：

International conference on bioengineering and biomedical signal and image processing

会议地点：

Meloneras(ES)

会议母体文献：

Bioengineering and biomedical signal and image processing

页码：

392-404

出版时间：

2021

DOI：

10.1007/978-3-030-88163-4_34