Brain Tumor Detection And Segmentation Using Deep Learning Approaches

Hemalatha T

doi:10.71097/IJSAT.v16.i2.6658

Brain Tumor Detection And Segmentation Using Deep Learning Approaches

Author(s)	Ms. Hemalatha T
Country	India
Abstract	Brain tumor detection and segmentation from MRI images are critical tasks for early diagnosis and effective treatment planning in medical imaging. This project aims to develop an advanced deep learning-based framework for automatic tumor classification and segmentation, leveraging state-of-the-art neural network architectures, namely MobileNet and DenseNet, to improve detection accuracy and computational efficiency. MobileNet's lightweight design facilitates real-time applications by reducing model complexity without sacrificing performance, while DenseNet's densely connected layers enhance feature propagation, leading to more robust and precise classification outcomes.The system is designed to classify brain MRI images into two categories: tumor and non-tumor. The classification networks employ MobileNet and DenseNet to maximize accuracy and optimize computational resources. MobileNet provides a streamlined approach suitable for edge computing and mobile devices, ensuring faster inference times, while DenseNet's improved gradient flow contributes to higher detection accuracy.For segmentation tasks, the framework can be extended to localize tumor regions within the brain, potentially using complementary segmentation techniques. The integration of these models aims to enhance diagnostic capabilities by providing automated, reliable, and accurate tumor detection to support clinical decision-making. This approach holds promise for improving early diagnosis, reducing the need for invasive diagnostic procedures, and potentially integrating into real-time diagnostic systems in healthcare settings.The project will evaluate the proposed methods using benchmark datasets, with performance metrics including accuracy, precision, recall, and segmentation quality to validate its effectiveness in real-world medical imaging scenarios.
Keywords	Brain Tumor, MRI, MobileNet, DenseNet, Deep Learning, Accuracy and Robust.
Field	Engineering
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-06-10
DOI	https://doi.org/10.71097/IJSAT.v16.i2.6658
Short DOI	https://doi.org/g9r8d8

View / Download PDF File

doi

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

IJSAT DOI prefix is
10.71097/IJSAT

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 16 Isu 3 Cover Page Vol 16 Isu 2 Cover Page Vol 16 Isu 1

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 IJSAT 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	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology

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

Brain Tumor Detection And Segmentation Using Deep Learning Approaches

Share this