Problems of Deploying CNN Transfer Learning to Detect COVID-19 from Chest X-rays

Abstract

The Covid-19 first occurs in Wuhan, China in December 2019. After that the virus spread all around the world and at the time of writing this paper the total number of confirmed cases are above 4.7 million with over 315000 deaths. Machine learning algorithms built on radiography images can be used as a decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is to conduct a critical analysis to investigate the applicability of convolutional neural networks (CNNs) for the purpose of COVID-19 detection in chest X-ray images and highlight the issues of using CNN directly on the whole image. To achieve this task, we first use 12-off-the-shelf CNN architectures in transfer learning mode on 3 publicly available chest X-ray databases together with proposing a shallow CNN architecture in which we train it from scratch. Chest X-ray images fed into CNN models without any preprocessing to follow the many of researches using chest X-rays in this manner. Next, a qualitative investigation performed to inspect the decisions made by CNNs using a technique known as class activation maps (CAM). Using CAMs, one can map the activations contributed most to the decision of CNNs back to the original image to visualize the most discriminating regions on the input image. We conclude that CNN decisions should not be taken into consideration, despite their high classification accuracy, until clinicians can visually inspect, and approve, the region(s) of the input image used by CNNs that lead to its prediction.

Transfer Learning-Based Automatic Detection of Coronavirus Disease 2019 (COVID-19) from Chest X-ray Images

“…It must be analyzed and diagnosed from chest X-ray images by using an expert radiologist. Of note, it is time-consuming and has susceptibility to detect erroneously [ 11 ]. Therefore, the automatically detect of COVID-19 from chest X-ray images is required.…”

Applications of artificial intelligence in battling against covid-19: A literature review

“…Other examples of the research that use CNN for detecting covid-19 via CT images include [295] , [296] , [297] , [298] , [299] , [300] , [301] , [302] , [303] , [304] , [305] , [306] , [307] , [308] , [309] , [310] .…”

Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images

“…CNN models have shown better performance in various applications such as agriculture, industry, and diagnosis of medical diseases (Rahimzadeh and Attar 2020a;Ghosh et al 2020;Dekhtiar et al 2018). The architecture of CNN imitates the visual cortex system of humans (Majeed et al 2020;Basavegowda and Dagnew (2020)). CNN architecture is shown in Fig.…”