Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach

Web-enabled e-healthcare system or computer assisted disease diagnosis has a potential to improve the quality and service of conventional healthcare delivery approach. The article describes the design and development of a web-based distributed healthcare management system for medical information and quantitative evaluation of microscopic images using machine learning approach for malaria. In the proposed study, all the health-care centres are connected in a distributed computer network. Each peripheral centre manages its' own health-care service independently and communicates with the central server for remote assistance. The proposed methodology for automated evaluation of parasites includes pre-processing of blood smear microscopic images followed by erythrocytes segmentation. To differentiate between different parasites; a total of 138 quantitative features characterising colour, morphology, and texture are extracted from segmented erythrocytes. An integrated pattern classification framework is designed where four feature selection methods viz. Correlation-based Feature Selection (CFS), Chi-square, Information Gain, and RELIEF are employed with three different classifiers i.e. Naive Bayes', C4.5, and Instance-Based Learning (IB1) individually. Optimal features subset with the best classifier is selected for achieving maximum diagnostic precision. It is seen that the proposed method achieved with 99.2% sensitivity and 99.6% specificity by combining CFS and C4.5 in comparison with other methods. Moreover, the web-based tool is entirely designed using open standards like Java for a web application, ImageJ for image processing, and WEKA for data mining considering its feasibility in rural places with minimal health care facilities.