Automated Segmentation of Computed Tomography Images for COVID-19 Patient Evaluation

COVID-19, caused by SARS-COV-2, resulted in 774 million cases and 7 million deaths by March 2024. This study proposes an approach to detect pulmonary lesions in computed tomography scans, integrating classification, preprocessing, and segmentation. Initially, a model based on LeNet-5 classifies the relevant slices of the scans, eliminating the irrelevant ones. Subsequently, the selected images undergo contrast adjustments, binarization, and normalization. Afterwards, segmentation is performed using a U-Net-based architecture, allowing for detailed segmentation. The methodology achieved 78.40% Dice, 64.80% IoU, 78% Sensitivity, 100% Specificity, 89.60% AUC, and 81% Precision, using only 9 million parameters. These results offer a practical and efficient solution, supporting specialists in patient treatment.