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 CXR images with reduced computational time and promising results. Our proposed model was thoroughly evaluated and compared with existing models. Methods A retrospective study was conducted to analyze 5 public data sets containing 4200 CXR images using machine learning techniques including decision trees, support vector machines, and neural networks. The images were preprocessed to undergo image segmentation, enhancement, and feature extraction. The best performing machine learning technique was selected and combined into a multilayer hybrid classification model for COVID-19 (MLHC-COVID-19). The model consisted of 2 layers. The first layer was designed to differentiate healthy individuals 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 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.

Malaria is a life-threatening mosquito-borne disease. Recently, the number of malaria cases has increased worldwide, threatening vulnerable populations. Malaria is responsible for a high rate of morbidity and mortality in people all around the world. Each year, many people, die from this disease, according to the World Health Organization (WHO). Thick and thin blood smears are used to determine parasite habitation and computer-aided diagnosis (CADx) techniques using machine learning (ML) are being used to assist. CADx reduces traditional diagnosis time, lessens socio-economic impact, and improves quality of life. This study develops a simplified model with selective features to reduce processing power and further shorten diagnostic time, which is important to resource-constrained areas. To improve overall classification results, we use a decision tree (DT)-based approach with image pre-processing called optimal features to identify optimal features. Various feature selection and extraction techniques are used, including information gain (IG). Our proposed model is compared to a benchmark state-of-art classification model. For an unseen dataset, our proposed model achieves accuracy, precision, recall, F-score, and processing time of 0.956, 0.949, 0.964, 0.956, and 9.877 s, respectively. Furthermore, our proposed model’s training time is less than those of the state-of-the-art classification model, while the performance metrics are comparable.

BACKGROUND 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 (CXR) 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 have been much less discussed. OBJECTIVE The motivation of our work is to develop a new computer-aided diagnosis (CADx) tool for identifying CXR images of COVID-19 infection using multiple machine learning techniques based on multi-layer classification architecture. It operated under the condition of minimal computational time with promising classification results. METHODS In this retrospective study, five public datasets of 4,200 CXR images were analyzed using multiple machine learning techniques which include decision tree (DT), support vector machine (SVM), and neural networks (NNs). First, image segmentation, image enhancement, and feature extraction techniques were performed. Second, machine learning techniques were selected based on classification performance. Finally, the selected machine learning techniques were 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. RESULTS The MLHC-COVID-19 is evaluated with real COVID-19 cases from various databases. The classification results show promising performance with minimum processing time achieving accuracy, sensitivity, and specificity of 0.962, 0.962, and 0.971, respectively. This demonstrates the effectiveness of the MLHC-COVID-19 in classifying CXR images, enhancing the accuracy of CXR image interpretation with a reduction in the interpretation time. A web-based MLHC-COVID-19 CADx (http://psuva.com/mlhc) has been developed for public use. CONCLUSIONS MLHC-COVID-19 performed with promising classification results. A comparison between the MLHC-COVID-19 and other state-of-the-art DL techniques has been presented and discussed. MLHC-COVID-19 could be improved in future work. Some limitations are clearly stated. The entire process is semi-automated. The CXR raw images must be pre-processed before being classified with MLHC-COVID-19.