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

  • Patient Facing Systems
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Abstract

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.

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Acknowledgments

The authors thank the Dept. of Pathology, Midnapur Medical College and Hospital for providing the pathological images and clinical know-how. The first author acknowledges the Council of Scientific and Industrial Research for providing financial support to carry out this research work under the award no. 09/81(1223)/2014/EMR-I dt. 12-08-2014. Authors also acknowledge to Dept. of Information Technology, Govt. of India for financial assistance (Ref. No. IIT/SRIC/SMST/DPR/2009-10/15) to carry out the work.

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Authors and Affiliations

  1. School of Medical Science & Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India

    Maitreya Maity, Dhiraj Dhane, Tushar Mungle & Chandan Chakraborty

  2. Department of Pathology, Midnapur Medical College and Hospital, Medinipur, West Bengal, India

    A. K. Maiti

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  1. Maitreya Maity
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  2. Dhiraj Dhane
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Correspondence to Chandan Chakraborty.

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Maity, M., Dhane, D., Mungle, T. et al. Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach. J Med Syst 41, 192 (2017). https://doi.org/10.1007/s10916-017-0834-0

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