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 Powered Tonsillitis Detection Using Throat Image Analysis and Hybrid Computer Vision Techniques |
|---|---|
| ABSTRACT | To improve tonsillitis screening techniques to be accurate and efficient, the design and development of an AI-powered tonsillitis detection system using picture processing and computer vision techniques are presented in this paper. The focus of the proposed system is the automated analysis of throat pictures to highlight visual indicators such as redness, swelling, and white patches on the tonsils. The methodology primarily focuses on structured image preprocessing, reliable feature extraction, and effective severity classification. The system has been designed to be scalable and keep results consistent while allowing local client-side processing for enhanced privacy. Thus the proposed approach has been effectively evaluated and shown to lead to an increase in accuracy of detection and decrease in manual dependence, presently making it ideal for educational, pre-diagnostic, and real-world screening applications. |
| AUTHOR | VEENA H S, MALATESH K K, M H VIRATAIAH, VIJAY B AGADI, VINAYAKA GONAL Assistant Professor, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India UG Students, Dept. of CSE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312105 |
| pdf/105_AI Powered Tonsillitis Detection Using Throat Image Analysis.pdf | |
| KEYWORDS | |
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