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.
