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 | Explainable AI System for Brain Tumor Detection & Severity Prediction |
|---|---|
| ABSTRACT | Brain tumor diagnosis is a critical requirement in medical imaging and oncology. Explainable artificial intelligence can significantly enhance early diagnosis and severity prediction of brain tumors when applied to MRI scans through advanced deep learning models. By processing brain MRI images and extracting tumor-specific features, AI models can classify tumors and estimate their severity levels, such as low-grade and high-grade tumors. To ensure transparency and clinical reliability, explainability methods like Grad-CAM and SHAP are used to highlight the most influential MRI regions and feature contributions responsible for the prediction. These interpretations allow medical professionals to verify the AI’s reasoning, improving trust and enabling informed decision-making. This approach supports precise tumor assessment, reduces diagnostic ambiguity, and strengthens the role of interpretable AI in real-world healthcare applications involving brain tumor analysis using MRI technology. |
| AUTHOR | AKASH K L, DHANUSHA K, PRAFFUL KUMAR S A, REHAN MALLICK, PROF. SHANKAR SARJI P U.G. Student, Department of CS&E, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India Assistant Professor, Department of CS&E, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312063 |
| pdf/63_Explainable AI System for Brain Tumor Detection & Severity Prediction.pdf | |
| KEYWORDS | |
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