AI POWERED BRAIN TUMOR DETECTION AND MEDICAL INTERPRETATION USING DEEP LEARNING
Maanasvi R
, Sruthi B , Nithilaks Shri T , Geetha C
Brain Tumor Detection, Deep Learning, MRI, Image Processing, Convolutional Neural Networks, Segmentation, Artificial Intelligence
Detecting brain tumors is one of the most important and difficult things to do in medical image analysis. This is because early diagnosis is very important for improving patient survival rates and treatment effectiveness. In conventional medical practices, the detection of brain tumors is conducted through the manual interpretation of MRI images by radiologists, a process that is labor-intensive and heavily reliant on specialized expertise. Also, manual diagnosis is often wrong because people make mistakes, especially when they have to deal with a lot of medical data or complicated tumor structures. Because of this, there is a growing need for smart, automated systems that can help doctors find brain tumors accurately and give them reliable results quickly.
In the last few years, artificial intelligence and deep learning techniques have made big changes to the field of medical imaging. Deep learning models, especially convolutional neural networks, have shown that they can do an amazing job of extracting complex features from MRI images and doing accurate classification tasks. Segmentation techniques have become more important in addition to classification because they help doctors find the exact location and edges of tumors. This improves medical interpretation and helps doctors make better clinical decisions.
This paper proposes an AI-driven system for brain tumor detection and medical interpretation utilizing deep learning methodologies. The proposed system combines modules for image preprocessing, classification, and segmentation into a single framework to make sure that MRI images are analyzed quickly and accurately. The system lets users upload MRI images through a web-based interface. The images are processed in real time, and the results of the predictions are available right away. The segmentation module makes the output even better by highlighting the tumor area, which makes it easier to understand.
"AI POWERED BRAIN TUMOR DETECTION AND MEDICAL INTERPRETATION USING DEEP LEARNING", JETNR - JOURNAL OF EMERGING TRENDS AND NOVEL RESEARCH (www.JETNR.org), ISSN:2984-9276, Vol.4, Issue 4, page no.b856-b863, April-2026, Available :https://rjpn.org/JETNR/papers/JETNR2604245.pdf
Volume 4
Issue 4,
April-2026
Pages : b856-b863
Paper Reg. ID: JETNR_233873
Published Paper Id: JETNR2604245
Downloads: 00043
Research Area: Humanities All
Country: Chennai , Tamil Nadu, India
ISSN: 2984-9276 | IMPACT FACTOR: 9.87 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.87 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: RJPN (IJPublication) Janvi Wave
