Abstract:
This study addresses the need for rapid and accurate COVID-19 diagnosis by
utilizing Convolutional Neural Networks (CNNs) on CT chest images. Given the
limitations of traditional RT-PCR testing, CT imaging has emerged as a highly
sensitive diagnostic tool, but manual analysis can be time-consuming and error-
prone. This research investigates the impact of data augmentation and median
filtering on COVID-19 classification performance using CT images. Experiments
were conducted with two pre-trained CNN models DenseNet121 and
DenseNet169 across three conditions: (1) COVID with data augmentation, (2)
COVID without data augmentation, and (3) COVID with median filter and data
augmentation
