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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: May 2026
Volume 11 | Issue 5
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 120
Article Submitted : 24205
Article Published : 9239
Total Authors : 24597
Total Reviewer : 847
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| Paper Title: | NeuroVision-MRI Based Brain Tumor Detection System |
| Authors Name: | M.K.V.Anvesh , Gedela Lavanya , Gorle Prasanna , Gavenkula Lavanya , Dumpa Pavithra |
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IJRTI_211733
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| Published Paper Id: | IJRTI2604201 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Brain Tumor (BT) is a critical neurological disorder that requires early and accurate diagnosis to improve patient survival and treatment outcomes. Recent advancements in deep learning and medical imaging have enabled automated analysis of Magnetic Resonance Imaging (MRI), offering a reliable alternative to manual interpretation, which is often time-consuming and dependent on radiologist expertise. This project proposes a comprehensive deep learning–based framework for multi-class brain tumor classification using MRI images, categorized into four classes: glioma, meningioma, pituitary tumor, and no tumor. The dataset was collected from publicly available medical imaging repositories and preprocessed to enhance image quality and model performance. We conducted an extensive evaluation using multiple state-of-the-art architectures, including EfficientNet variants (B0–B6), VGG16, Xception, and transformer-based models such as MaxViT, to identify the most effective model for tumor classification. Experimental results demonstrate that the proposed framework achieves a high classification accuracy of 97%, indicating strong generalization and robustness across all tumor categories. To ensure clinical interpretability, Explainable AI (XAI) techniques such as Grad-CAM were employed to visualize the regions of interest influencing model predictions. This approach emphasizes the potential of deep learning–based systems as scalable and cost-effective tools for assisting radiologists in early brain tumor detection and classification, enabling timely clinical decision-making. Furthermore, the system can be enhanced by integrating patient clinical data and deploying it as a web-based application for real-time diagnostic support in healthcare environments. |
| Keywords: | Brain Tumor Detection, Magnetic Resonance Imaging (MRI), Deep Learning, Multi-Class Classification, EfficientNet, Xception, Explainable AI (XAI), Medical Image Analysis. |
| Cite Article: | "NeuroVision-MRI Based Brain Tumor Detection System", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b477-b481, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604201.pdf |
| Downloads: | 000205508 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604201
Registration ID:211733
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: b477-b481 Country: Visakhapatnam, Andhra Pradesh, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604201 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604201 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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