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 Leukemia Diagnosis Using Deep Learning Models |
|---|---|
| ABSTRACT | Leukemia is a life threatening hematological disease that requires early and accurate diagnosis for effective treatment. Conventional diagnostic practices rely on microscopic examination of blood smear images by medical experts, a process that is time consuming and prone to subjective interpretation. To address these limitations, this paper proposes an automated leukemia detection framework based on deep learning techniques. A Convolutional Neural Network (CNN) is employed to analyze microscopic blood smear images and identify malignant patterns directly from raw image data. To further enhance classification performance, Particle Swarm Optimization (PSO) is integrated to optimize learning parameters. The proposed approach aims to improve diagnostic accuracy, reduce manual effort, and support clinicians by providing consistent and reliable results. Experimental observations suggest that the system has strong potential for application in computer aided leukemia diagnosis. |
| AUTHOR | PROF. NAVYA K G, GIRISH B, RIDA AJMAYEEN, SHARAN I S, SRI ABHINAV Department of Computer Science and Design, Bapuji Institute of Engineering and Technology, Davangere, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312099 |
| pdf/99_AI Powered Leukemia Diagnosis Using Deep Learning Models.pdf | |
| KEYWORDS | |
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