Authors

Sugan M

Department of Electronics and Communication Engineering, K.L.N. College of Engineering, Tamilnadu, India

Helen Sunomitha Nancy

Department of Electronics and Communication Engineering, K.L.N. College of Engineering, Tamilnadu, India

Abstract

Brain tumors, a leading cause of mortality, necessitate accurate and timely detection for effective treatment. Recent advancements in deep learning and medical imaging have significantly enhanced the capability to identify and classify cancers. This extensive study focuses on three specific tumor types: glioma, meningioma, and pituitary tumors and explores the application of deep transfer learning methods for brain tumor classification utilizing convolutional neural networks (CNN). By analyzing MRI scans, the research assesses the performance of pretrained CNN models, including ResNet-50, Inception-v3, and VGG-16 in automating tumor prediction. The study employs performance metrics such as accuracy, precision, recall, and F1 scores to evaluate how effectively each model aids in early diagnosis and improves clinical decision-making. 

Keywords

MRI Convolutional Neural Network (CNN) Deep Transfer Learning Brain Tumor Custom CNN Inception-v3 Tumor Classification

Citation of this Article

Sugan M, & Helen Sunomitha Nancy. (2025). Employing CNN Deep Learning for the Detection and Classification of Multiple Types of Brain Tumors. Current Journal of Engineering and Science Research. 2(3), 6-12. Article DOI: https://doi.org/10.47001/CJESR/2025.203002

Licence Copyright (c) 2026 Current Journal of Engineering and Science Research. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

  1. Shaiq Wani, Sachin Ahuja, Abhishek Kumar “A review on Brain Tumor Detection using Deep Neural Networks” IEEE 2023.
  2. Romaisa Alqaran, Safa Alyasein, Ahmad M. Mustafa “Brain Tumor Detection Using Deep Learning” IEEE 2023.
  3. K. Nuresa Qodri, I. Soesanti and Ha. Adi Nugroho “Image Analysis for MRIBASED Brain Tumor Classification Using Deep Learning” IJITEE, March 2021.
  4. A.Abougarair and Abdulhamid Oun “Implementation of a Brain Computer Interface for Robotic Arm Control”, 2021 IEEE 1st International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MISTA2021),25-27 may,2021, Tripoli, Libya.
  5. How to Implement Classification In Machine Learning, https://www.edureka.co/blog/classification-in-machine-learning/
  6. M. Aburakhis and A. Abougarair, Adaptive Neural Networks Based Robust Output Feedback Controllers for Nonlinear Systems, International Journal of Robotics and Control Systems, Vol. 2, No. 1,2022, pp. 37-56, ISSN: 2775-2658, http://pubs2.ascee.org/index.php/ijrcs
  7. Machine Learning, Sensitivity vs Specificity Difference https://vitalflux.com/ml-metrics-sensitivity-vs-specificity-difference/
  8. The buzz about artificial intelligence, machine learning and deep learning: An overview, https://fusionalliance.com/artificialintelligence-machine-learning-and-deep-learningoverview
  9. Ahmed J. Abougarair, Mohamed K. Aburakhis, Design and implementation of smart voice assistant and recognizing academic words, International Robotics & Automation Journal, Volume 8 Issue1 – 2022.
  10. Masood, M. Furqan, Tahira Nazir, Marriam Nawaz, Awais Mehmood, Junaid Rashid, Hyuk-Yoon Kwon, Toqeer Mahmood and Amir Hussain. “A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images.” Diagnostics 11 (2021).
  11. Sahithi, K., D. Krishna Sai and D. Sameera. “Detection of Brain Tumors using Neural Networks.” (2020).
  12. Anjum, Sadia, Lal Hussain, Mushtaq Ali, Monagi H. Alkinani, Wajid Aziz, Sabrina Gheller, Adeel Ahmed Abbasi, Ali Raza Marchal, Harshini Suresh and Tim Q. Duong. “Detecting brain tumors using deep learning convolutional neural network with transfer learning approach.” International Journal of Imaging Systems and Technology 32 (2021): 307 - 323.
  13. Arabahmadi, Mahsa, Reza Farahbakhsh and Javad Rezazadeh. “Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging.” Sensors (Basel, Switzerland) 22 (2022).
  14. Abdalla, Hussna Elnoor Mohammed and Mohammed Yagoub Esmail. “Brain Tumor Detection by using Artificial Neural Network.” 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE) (2018): 1-6.
  15. Tiwari, Pallavi, Bhaskar Pant, Mahmoud M Elarabawy, Mohammed Abd-Elnaby, Noorjahan Banu Mohd, Gaurav Dhiman and Subhash Sharma. “CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.” Computational Intelligence and Neuroscience 2022 (2022).