M. Kaliappan
Papers
1
Total Citations
16
H-Index
1
About
M. Kaliappan is a researcher whose work centers on medical image analysis and machine learning applications for disease detection, particularly in ophthalmology. Their most cited paper, "A method of progression detection for glaucoma using K-means and the GLCM algorithm toward smart medical prediction" (2021), has garnered 16 citations, reflecting a focused contribution to the development of automated diagnostic tools. This study introduced a novel approach combining K-means clustering with Gray-Level Co-occurrence Matrix (GLCM) texture analysis to detect glaucoma progression, aiming to enhance early intervention through smart medical prediction systems. While the paper was later retracted, it underscores Kaliappan's engagement with pressing challenges in computational healthcare, specifically the translation of algorithmic methods into clinical decision support. Their work sits at the intersection of computer vision, pattern recognition, and biomedical engineering, offering a pathway toward more accessible and efficient screening for vision-threatening conditions. For students and researchers exploring AI-driven diagnostics, Kaliappan’s research highlights both the potential and the complexities of applying machine learning to real-world medical problems.
Research Focus
Key Achievements
Top Papers
- 1