Data augmentation and denoising of computed tomography scan images in training deep learning models for rapid COVID-19 detection Online publication date: Thu, 01-Feb-2024
by Auwalu Saleh Mubarak; Sertan Serte; Fadi Al-Turjman; Zubaida Sa'id Ameen
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 24, No. 2, 2024
Abstract: The deadly respiratory disease corona virus-2 (COVID-19) which was declared a pandemic by the World Health Organization (WHO) has resulted in over a million deaths around the world within less than a year. With the rapid spread of the virus, the currently adopted COVID-19 test by the WHO is the reverse transcription polymerase chain reaction (RT-PCR) test, which is expensive, time-consuming and not accessed by underdeveloped countries. Computed tomography (CT) scan images that were used in profiling suspected COVID-19 patients can serve as an alternative to the RT PCR test method. In this study, two different pre-trained deep learning models ResNet-50 and ResNet-101 were trained to classify positive COVID-19 scan images. The best model which was trained on the augmented CT scan images achieved an accuracy of 98.3%, a sensitivity of 0.984, specificity of 0.983.
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