Novel pre-processing stage for classification of CT scan Covid-19 images

Abstract

An accurate evaluation of computed tomography (CT) chest images is crucial in the early-stage detection of Covid-19. The accuracy of a diagnosis is determined by the imaging modality used and the images' consistency. This paper describes a gradient-based enhancement algorithm (GCE) for CT images that can increase the visibility of the infected region. Using a multi-scale dependent dark pass filter aims to increase contrast while preserving information and edge details of the infected area. Joint occurrence between the edge details and pixel intensities of the input image is calculated to construct a cumulative distribution function (CDF). To obtain the contrast improved image, the CDF is mapped to the uniform distribution. The GCE approach is tested on the CT Covid database, and performance metrics like the contrast improvement index (CII), discrete entropy (DE), and Kullback-Leibler distance (KL-Distance) are used to evaluate the results. Compared to other techniques available in the literature, the GCE approach produces the highest CII and DE values and has more uniformity. To check the suitability of the enhancement algorithm in terms of pre-processing, a pre-trained AlexNet is employed for the classification of Covid-19 images. The finding shows an improvement of 7% in classification accuracy after enhancing the Covid-19 images using the GCE technique.