Name
Title | ||
|---|---|---|
Web-Enabled Distributed Health-Care Framework for Automated Malaria Parasite Classification: an E-Health Approach. |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s10916-017-0834-0 | J. Medical Systems |
Keywords | Field | DocType |
Computer-aided diagnosis,Electronic healthcare system,Feature extraction,Feature selection,Malaria screening,Supervised classification | Telemedicine,Data mining,Feature selection,Naive Bayes classifier,Segmentation,Image processing,Feature extraction,Artificial intelligence,Web application,Medicine,Machine learning,The Internet | Journal |
Volume | Issue | ISSN |
41 | 12 | 1573-689X |
Citations | PageRank | References |
2 | 0.43 | 14 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maitreya Maity | 1 | 10 | 1.68 |
| Dhiraj Dhane | 2 | 2 | 0.43 |
| Tushar Mungle | 3 | 9 | 2.15 |
| Asok Kumar Maiti | 4 | 6 | 1.66 |
| Chandan Chakraborty | 5 | 537 | 50.60 |