Authors : S. Abhignya; D. Shubhang; Dr. D. Sujatha

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/2a8jpyta

DOI : https://doi.org/10.38124/ijisrt/26jul963

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In this paper, an efficient deep learning-based system is designed to detect brain tumors from MRI images. This system uses a multi-class classification approach, which can classify brain tumors into categories such as Glioma, Meningioma, Pituitary Tumor, and No Tumor cases using a model built upon EfficientNet-B0. To ensure transparency, Grad-CAM is applied in this study to visualize the portions of the input image that help to make predictions, thus making the results easily interpretable in terms of locating a brain tumor. Moreover, besides classification, tumor segmentation is carried out for Glioma brain tumors through the use of V-Net model built upon the BraTS 2020 Dataset. Finally, tumor coverage calculation and automated medical reports are provided by the developed system.

Keywords : Brain Tumor Detection, Deep Learning, MRI Analysis, EfficientNet-B0, V-Net, Grad-CAM, Segmentation, Tumor Classification.

References :

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