An Automated Computed Tomography Scan Analysis Framework for COVID-19 Detection Using Machine Learning

During the coronavirus disease-19 (COVID-19) epidemic, there has been a growing need for rapid diagnostic tools, with Computed Tomography (CT) scans emerging as essential diagnostic resources. Nevertheless, the process of manually interpreting their findings, although informative, is nevertheless characterized by a significant amount of work and variability. In the current study, we intend to construct a machine learning-based model to automate the evaluation of CT images for COVID-19 diagnosis and to differentiate it from pneumonia and other non-COVID diseases. The model we propose employs a Tolerant Local Median Fuzzy C-means (TLMFCM) segmentation strategy in conjunction with the Stacked Sparse Autoencoder (SSAE) for robust feature extraction. The classification task employs a Locally Controlled Seagull Kernel Extreme Machine Learning (LCS-KELM) whose parameters are optimized with the Seagull Optimization algorithm (SOA). Our model performed better than other models in preliminary comparisons against traditional benchmarks, with an accuracy of 96.3% and a faster processing time.