METHODOLOGY FOR IMPROVING DEEP LEARNING-BASED CLASSIFICATION FOR CT SCAN COVID-19 IMAGES

Abstract

Early identification of COVID-19 necessitates a precise interpretation of computed tomography (CT) chest images. Consistent and accurate photographs are critical for the correct diagnosis. In this paper, a novel pre-processing approach (mainly improves the contrast) based on gradient enhancement method (GCE) is proposed for prominent visualization of the diagnostic features from the COVID-19 CT images. This pre-processing stage helps in preserving the diagnostic information in the disease affected area. Edge information in the CT images helps the physicians and classification model for better classification. The edge features are preserved by improving the contrast by using multi-scale dependent dark pass filter. From the edge features and pixel intensities, cumulative distribution function (CDF) is computed. It is then mapped to a uniform distribution, resulting in contrast enhanced COVID-19 CT images. These pre-processed images are fed to the customized deep convolutional neural networks (CNNs) like AlexNet, VGG-19, ResNet-101, DenseNet-201, GoogleNet, MobileNet-v2, SqueezeNet, Inception-v3, Xception, and EfficientNet-b0 for classification. Introducing GCE as a pre-processing stage improves the COVID-19 classification accuracy by nearly 6%. Evaluation of the contrast enhancement by GCE technique is carried out on the CT images by contrast improvement index (CII), discrete entropy (DE), and Kullback–Leibler distance (KL-Distance) measures. The experimental findings reveal that the GCE method produces higher CII and DE values than the other enhancement methods available.