Brain Tumor Detection Using RestNet50 Architecture

Year : 2025 | Volume : 15 | Issue : 02 | Page : 1 13
    By

    Rishabh Gupta,

  • Saurabh Kumar Yadav,

  • Shadab Manzar Khan,

  1. Student, Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  2. Student, Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  3. Student, Department of Computer Science and Engineering, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India

Abstract

This paper presents a novel deep learning model for brain tumor diagnosis from MRI scans on the basis of ResNet50 with some modifications. Optimizing the modified layers and pre-trained ResNet50 for improved diagnostic accuracy and reliability in real-world clinical settings is one of the key contributions of this paper. The model was trained on an extremely well-balanced data of 2,577 MRI scans, which were split equally among the tumor and non-tumor patients. With an aim for gaining greater model robustness over a wide range of clinical setups, extensive use of data augmentation methods was utilized. The strategy gained an exceptionally high-test accuracy of 97.35%, coupled with high precision, recall, and F1-scores, thus demonstrating its effectiveness in detecting tumor cases with high confidence. The results establish the practicability of the model as an efficient tool for alleviating the radiologists’ workload in diagnosis and assisting in decision-making through provision of a second opinion. The method enhances the precision in the detection of brain tumors by fusing the state-of-the-art of deep learning with medical imaging. It also provides a foundation for the early detection and improvement of treatment planning. The pragmatic realities of clinical implementation and the adaptability of the model across various health settings are what the future work will concentrate on.

Keywords: Artificial intelligence, magnetic resonance imaging (MRI), deep convolutional neural networks (CNNs), brain tumour detection, ResNet50

[This article belongs to Current Trends in Signal Processing ]

How to cite this article:
Rishabh Gupta, Saurabh Kumar Yadav, Shadab Manzar Khan. Brain Tumor Detection Using RestNet50 Architecture. Current Trends in Signal Processing. 2025; 15(02):1-13.
How to cite this URL:
Rishabh Gupta, Saurabh Kumar Yadav, Shadab Manzar Khan. Brain Tumor Detection Using RestNet50 Architecture. Current Trends in Signal Processing. 2025; 15(02):1-13. Available from: https://journals.stmjournals.com/ctsp/article=2025/view=213576


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Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 08/05/2025
Accepted 16/05/2025
Published 19/06/2025
Publication Time 42 Days
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