Detecting Coronavirus from Chest X-rays Using Transfer Learning

Abstract

Coronavirus disease (COVID-19) is an illness caused by a novel coronavirus family. One of the practical examinations for COVID-19 is chest radiography. COVID-19 infected patients show abnormalities in chest X-ray images. However, examining the chest X-rays requires a specialist with high experience. Hence, using deep learning techniques in detecting abnormalities in the X-ray images is presented commonly as a potential solution to help diagnose the disease. Numerous research has been reported on COVID-19 chest X-ray classification, but most of the previous studies have been conducted on a small set of COVID-19 X-ray images, which created an imbalanced dataset and affected the performance of the deep learning models. In this paper, we propose several image processing techniques to augment COVID-19 X-ray images to generate a large and diverse dataset to boost the performance of deep learning algorithms in detecting the virus from chest X-rays. We also propose innovative and robust deep learning models, based on DenseNet201, VGG16, and VGG19, to detect COVID-19 from a large set of chest X-ray images. A performance evaluation shows that the proposed models outperform all existing techniques to date. Our models achieved 99.62% on the binary classification and 95.48% on the multi-class classification. Based on these findings, we provide a pathway for researchers to develop enhanced models with a balanced dataset that includes the highest available COVID-19 chest X-ray images. This work is of high interest to healthcare providers, as it helps to better diagnose COVID-19 from chest X-rays in less time with higher accuracy.

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models

“…Additional neural network details are shown in Table 2 . The DNN requires sorting out some issues related to its parameters, including the tuning of parameters, the growth of data size, and normalization [ 35 , 36 ].…”

LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images

“…There are lots of binary classification datasets, however, few works have treated multiclassification as more challenging compared to binary classifications. Dataset_1 : The ChestX-ray-15k dataset was acquired by Badawi et al [ 51 ] from eleven different sources. This dataset contains a balanced amount of Chest X-ray images for training/validation and testing, with 3,500 and 1,500 images, respectively.…”

“…Dataset_1 : The ChestX-ray-15k dataset was acquired by Badawi et al [ 51 ] from eleven different sources. This dataset contains a balanced amount of Chest X-ray images for training/validation and testing, with 3,500 and 1,500 images, respectively.…”

Indirect supervision applied to COVID-19 and pneumonia classification

“…The necessity for faster turnaround times to interpret radiographic images has led to a substantial effort to adopt CNN-based techniques, with a concentrated effort on distinguishing COVID-19 infected patients with the aid of both CT [ [15] , [16] , [17] , [18] , [19] , [20] , [21] ] and X-ray [ 14 , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] ] imaging. Several overviews into the application of CNN techniques to aid in COVID-19 diagnosis have been conducted and we refer the reader to Refs.…”