Multi-layer Hybrid Classification Model of COVID-19 Chest X-ray Images v1
The coronavirus disease of 2019 (COVID-19) has been declared a pandemic and has raised worldwide concern. Lung inflammation and respiratory failure are commonly observed in moderate-to-severe cases. Chest X-ray imaging is compulsory for diagnosis, and interpretation is commonly performed by skilled medical specialists. Many studies have been conducted using machine learning approaches such as Deep Learning (DL) with acceptable accuracy. However, other dimensions such as computational time were less discussed. Thus, our work is motivated to design anew computer-aided diagnosis (CADx) tool for identifying chest X-ray images of COVID-19 infection using machine learning techniques including Decision Tree (DT), Support Vector Machine (SVM), and Neural Networks (NNs). Our work is designed with the concept of multi-layer classification architecture and performs with minimal computational time and acceptable classification results. First, image segmentation, image enhancement and feature extraction techniques are performed. Second, machine learning techniques are selected based on classification performance. Finally, selected machine learning techniques are assembled into a multi-layer hybrid classification model for COVID-19 (MLHC-COVID-19). Specifically, the MLHC-COVID-19 consists of two layers, Layer I: Healthy and Unhealthy; Layer II: COVID-19 and non-COVID-19.