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 | Design and Development of Brain Tumor Pathology Classification Using EfficientNet-V2S with Explainable AI |
|---|---|
| ABSTRACT | Brain tumor detection using MRI images is a critical yet challenging task due to the limitations of manual interpretation and the lack of accuracy and transparency in existing machine learning and conventional CNN-based systems. To overcome these challenges, this project proposes a high- performance and interpretable AI-based brain tumor classification framework implemented using TensorFlow, leveraging EfficientNet-V2S, the highest architecture in the EfficientNet family. The model exploits compound scaling of network depth, width and resolution, enabling superior hierarchical feature extraction and improved diagnostic accuracy. To enhance clinical trust and usability, explainable AI techniques such as Grad-CAM integrated to visually highlight tumor-affected regions, allowing doctors to easily interpret and validate model predictions. Furthermore, the system is extended with a Generative AI–powered conversational interface, transforming the framework into an interactive clinical decision-support tool that explains diagnostic results beyond simple classification. By combining advanced deep learning and conversational intelligence, the proposed solution offers a transparent and clinician-centric approach that addresses existing gaps and significantly advances AI-assisted brain tumor diagnosis. |
| AUTHOR | DR. M. SENTHIL KUMAR, LOGESHWARAN S, RAKESH V, PRANESH K R Professor, Department of Artificial Intelligence and Data Science, Kongunadu College of Engineering and Technology, Trichy, India Department of Artificial Intelligence and Data Science, Kongunadu College of Engineering and Technology, Trichy, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1403014 |
| pdf/14_Design and Development of Brain Tumor Pathology Classification Using EfficientNet-V2S with Explainable AI.pdf | |
| KEYWORDS | |
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