In JMIR formative research
BACKGROUND : The COVID-19 pandemic has raised global concern, with moderate to severe cases displaying lung inflammation and respiratory failure. Chest X-ray (CXR) imaging is crucial for diagnosis and is usually interpreted by experienced medical specialists. Machine learning has been applied with acceptable accuracy, but computational efficiency has received less attention.
OBJECTIVE : We introduced a novel hybrid machine learning model to accurately classify COVID-19, non-COVID-19, and Healthy patients from chest X-ray (CXR) images with reduced computational time and promising results. Our proposed model was thoroughly evaluated and compared to existing models.
METHODS : A retrospective study was conducted to analyze 5 public datasets containing 4,200 chest X-ray (CXR) images using machine learning techniques including decision trees, support vector machines, and neural networks. The images were pre-processed to undergo image segmentation, enhancement, and feature extraction. The best-performing machine learning technique was selected and combined into a Multi-Layer Hybrid Classification model for COVID-19 (MLHC-COVID-19). The model consisted of two layers. The first layer was designed to differentiate healthy subjects from infected patients, while the second layer aimed to classify COVID-19 and non-COVID-19 patients.
RESULTS : The MLHC-COVID-19 model was trained and evaluated on unseen COVID-19 CXR images, achieving reasonably high accuracy and F-measures of 0.962 and 0.962, respectively. These results show the effectiveness of the MLHC-COVID-19 in classifying COVID-19 CXR images, with improved accuracy and a reduction in interpretation time. The model was also embedded into a web-based MLHC-COVID-19 computer-aided diagnosis (CADx) system, which was made publicly available.
CONCLUSIONS : The study found that the MLHC-COVID-19 model effectively differentiated CXR images of COVID-19 patients from those of healthy and non-COVID-19 individuals. It outperformed other state-of-the-art deep learning techniques and showed promising results. These results suggest that the MLHC-COVID-19 model could have been instrumental in early detection and diagnosis of COVID-19 patients, thus playing a significant role in controlling and managing the pandemic. Although the pandemic has slowed down, this model can be adapted and utilized for future similar situations. The model was also integrated into a publicly accessible web-based computer-aided diagnosis system.
Phumkuea Thanakorn, Wongsirichot Thakerng, Damkliang Kasikrit, Navasakulpong Asma