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The use of computer-aided diagnosis in the reliable and fast detection ofcoronavirus disease (COVID-19) has become a necessity to prevent the spread ofthe virus during the pandemic to ease the burden on the medical infrastructure.Chest X-ray (CXR) imaging has several advantages over other imaging techniquesas it is cheap, easily accessible, fast and portable. This paper explores theeffect of various popular image enhancement techniques and states the effect ofeach of them on the detection performance. We have compiled the largest X-raydataset called COVQU-20, consisting of 18,479 normal, non-COVID lung opacityand COVID-19 CXR images. To the best of our knowledge, this is the largestpublic COVID positive database. Ground glass opacity is the common symptomreported in COVID-19 pneumonia patients and so a mixture of 3616 COVID-19, 6012non-COVID lung opacity, and 8851 normal chest X-ray images were used to createthis dataset. Five different image enhancement techniques: histogramequalization, contrast limited adaptive histogram equalization, imagecomplement, gamma correction, and Balance Contrast Enhancement Technique wereused to improve COVID-19 detection accuracy. Six different Convolutional NeuralNetworks (CNNs) were investigated in this study. Gamma correction techniqueoutperforms other enhancement techniques in detecting COVID-19 from standardand segmented lung CXR images. The accuracy, precision, sensitivity, f1-score,and specificity in the detection of COVID-19 with gamma correction on CXRimages were 96.29%, 96.28%, 96.29%, 96.28% and 96.27% respectively. Theaccuracy, precision, sensitivity, F1-score, and specificity were 95.11 %, 94.55%, 94.56 %, 94.53 % and 95.59 % respectively for segmented lung images. Theproposed approach with very high and comparable performance will boost the fastand robust COVID-19 detection using chest X-ray images.

Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images