Detection of COVID’19 through low resolution CT scan images using Deep Learning

Abstract

Coronavirus emerged as a deadly disease in 2019 killing almost 6.25 million people, with its variants still being discovered. For timely medical treatment it is necessary to accurately and rapidly diagnose the disease. The main test used for diagnosis was “The Reverse Transcription Polymerase Chain Reaction (RT-PCR)” but due to limited availability of RT-PCR equipment at the time of outbreak, alternative methods were used to mitigate the damage. One of them was the computed tomography (CT) scans, a noninvasive imaging approach. Utilizing this CT data, deep learning (DL) models were developed to expedite the diagnostic procedure. Due to privacy concerns, the original CT scans were not shared with the public which caused hindrance in the research and development of accurate DL methods. Hence, datasets were made from secondary sources, either by extracting images from preprints or saving images in any other format than DICOM, which generated low resolution images. To address this issue, preprocessing techniques were applied to generate better results of the DL models. The pixel intensities in images are normalized such that they lie in the range of the actual values of a CT scan in the Hounsfield unit scale and then given as an input to the model. Diagnostic performance was assessed by F1-score (84%), AUC (94%) and Accuracy (81%), which is better than the performance achieved without pre-processing. This study proves that enhancing the image quality, through pre-processing techniques, can improve the results when good quality data is unavailable and accurate models can be made for detecting any disease at the time of the outbreak.