International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI-Based Disease Prediction Using Retinal Images |
|---|---|
| ABSTRACT | This project presents an AI-driven disease prediction system using retinal biomarkers and deep learning techniques for early disease detection. Retinal fundus images are analyzed to identify diseases such as diabetes, hypertension, cardiovascular, and neurological disorders. To overcome these limitations, the proposed system uses a Convolutional Neural Network (CNN) for automated disease detection and retinal abnormality analysis. Image preprocessing techniques such as normalization, noise reduction, and contrast enhancement improve image quality and model performance. Overall, the system promotes preventive healthcare through non-invasive retinal imaging and early disease identification. |
| AUTHOR | V.V.NIKAAS, C.VANESSA PG Scholar, Dept. of Master of Computer Applications, R.V.S. College of Engineering, Dindigul, Tamil Nadu, India Assistant Professor, Dept. of Master of Computer Applications, R.V.S. College of Engineering, Dindigul, Tamil Nadu, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.14060104 |
| pdf/104_AI-Based Disease Prediction Using Retinal Images.pdf | |
| KEYWORDS | |
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