The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computer Vision Conference (CVC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2022.01309109
PDF

Automated Brain Disease Classification using Transfer Learning based Deep Learning Models

Author 1: Farhana Alam
Author 2: Farhana Chowdhury Tisha
Author 3: Sara Anisa Rahman
Author 4: Samia Sultana
Author 5: Md. Ahied Mahi Chowdhury
Author 6: Ahmed Wasif Reza
Author 7: Mohammad Shamsul Arefin

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Brain MRI (Magnetic Resonance Imaging) classification is one of the most significant areas of medical imaging. Among different types of procedures, MRI is the most trusted one to detect brain diseases. Manual and semi-automated segmentations need highly experienced radiologists and much time to detect the problem. Recently, deep learning methods have taken attention due to their automation and self-learning techniques. To get a faster result, we have used different algorithms of Convolutional Neural Network (CNN) with the help of transfer learning for classification to detect diseases. This procedure is fully automated, needs less involvement of highly experienced radiologists, and does not take much time to provide the result. We have implemented six deep learning algorithms, which are InceptionV3, ResNet152V2, MobileNetV2, Resnet50, EfficientNetB0, and DenseNet201 on two brain tumor datasets (both individually and manually combined) and one Alzheimer’s dataset. Our first brain tumor dataset (total of 7,023 images-training 5,712, testing 1,311) has 99-100 percent training accuracy and 98-99 percent testing accuracy. Our second tumor dataset (total of 3,264 images-training 2,870, testing 394) has 100 percent training accuracy and 69-81 percent testing accuracy. The combined dataset (total of 10,000 images-training 8,000, testing 2,000) has 99-100 percent training accuracy and 98-99 percent testing accuracy. Alzheimer’s dataset (total of 6,400 images-training 5,121, testing 1,279, 4 classes of images) has 99-100 percent training accuracy and 71-78 percent testing accuracy. CNN models are renowned for showing the best accuracy in a limited dataset, which we have observed in our models.

Keywords: Brain MRI; tumor; deep learning; classification; transfer learning

Farhana Alam, Farhana Chowdhury Tisha, Sara Anisa Rahman, Samia Sultana, Md. Ahied Mahi Chowdhury, Ahmed Wasif Reza and Mohammad Shamsul Arefin, “Automated Brain Disease Classification using Transfer Learning based Deep Learning Models” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01309109

@article{Alam2022,
title = {Automated Brain Disease Classification using Transfer Learning based Deep Learning Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01309109},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01309109},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Farhana Alam and Farhana Chowdhury Tisha and Sara Anisa Rahman and Samia Sultana and Md. Ahied Mahi Chowdhury and Ahmed Wasif Reza and Mohammad Shamsul Arefin}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Computer Vision Conference
  • Healthcare Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org