Brain Tumor Detection Using Feature Based Classification And Deep Neural Network

Sri Priya E; Kaviya Priya K; Naresh Kannan S; Subashini R

doi:10.36948/ijfmr.2025.v07i04.51811

Brain Tumor Detection Using Feature Based Classification And Deep Neural Network

Author(s)	Ms. Sri Priya E, Ms. Kaviya Priya K, Mr. Naresh Kannan S, Ms. Subashini R
Country	India
Abstract	A tumor is a mass of abnormal cells that accumulate forming a tissue. These cells feed on normal cells and can destroy them, one of these tumors is brain tumor which affects the nervous cell, brain cell, and membranes that surrounds the brain. Worldwide it has an impact of affecting 3.5 per 100,000 individuals. A tumor is diagnosed via imaging, brain tumor is imaged with MRI. Detection of brain tumors is more important because it gives patients a better chance at successful treatment and recovery. Checking MRI scans manually takes time and can sometimes lead to mistakes. In this project, computer-based system was used for detecting brain tumors using deep learning algorithm called Convolutional Neural Network (CNN). Dataset from Kaggle was collected which contained around 3,264 MRI images. The main objective of this work is to present a comprehensive study using CNN architecture. The CNN was used for extracting data for training, targeting problems, for validation method and for quantitative performance. Primarily it looks for patterns in the image using special filters then finally undergoes Decision making network that figures out whether the tumor is present. CNN algorithm shows the complete pass of entire training dataset through epoch. Confusion matrix was done which shows the performance of the trained model by showing the counts of true positives, true negatives, false positive and false negative. Final model reached an accuracy of 96.47%, with high precision and recall, which means that it was both accurate and dependable in identifying tumors. This shows that deep learning, especially CNN can be a powerful tool to help doctors detect brain tumors more quickly and accurately. With more testing, this kind of system could be used in real hospitals to support radiologists and improve diagnosis.
Keywords	Brain Tumor, Convolutional Network, Confusion matrix, Machine Learning, Data Analysis, Healthcare.
Field	Engineering
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-23
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.51811
Short DOI	https://doi.org/g9t2gg

View / Download PDF File

E-ISSN 2582-2160

doi

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 4 Cover Page Vol 7 Isu 3 Cover Page Vol 7 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Brain Tumor Detection Using Feature Based Classification And Deep Neural Network

Share this