ORIGINAL RESEARCH article
Front. Med.
Sec. Precision Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1636059
This article is part of the Research TopicAI Innovations in Neuroimaging: Transforming Brain AnalysisView all 9 articles
Lightweight CNN for Accurate Brain Tumor Detection from MRI with Limited Training Data
Provisionally accepted
- 1National College of Business Administration and Economics, Multan, Pakistan
- 2Istanbul Topkapi Universitesi, Istanbul, Türkiye
- 3Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia
- 4TC Istanbul Rumeli Universitesi, Istanbul, Türkiye
- 5Qassim University, Buraydah, Saudi Arabia
- 6Istanbul Sabahattin Zaim University, Istanbul, Türkiye
- 7Istanbul Nisantasi University, Istanbul, Türkiye
- 8Applied Science Private University, Amman, Jordan
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Aim: This study aims to develop a robust and lightweight deep learning model for early brain tumor detection using magnetic resonance imaging (MRI), particularly under constraints of limited data availability. Objective: To design a CNN-based diagnostic model that accurately classifies MRI brain scans into tumor-positive and tumor-negative categories with high clinical relevance, despite a small dataset. Methods: A five-layer CNN architecture—comprising three convolutional layers, two pooling layers, and a fully connected dense layer—was implemented using TensorFlow and TFlearn. A dataset of 189 grayscale brain MRI images was used, with balanced classes. The model was trained over 10 epochs and 202 iterations using the Adam optimizer. Evaluation metrics included accuracy, precision, recall, F1 Score, and ROC AUC. Results: The proposed model achieved 99% accuracy in both training and validation. Key performance metrics, including precision (98.75%), recall (99.20%), F1-score (98.87%), and ROC-AUC (0.99), affirmed the model's reliability. The loss decreased from 0.412 to near zero. A comparative analysis with a baseline TensorFlow model trained on 1,800 images showed the superior performance of the proposed model. Conclusion: The results demonstrate that accurate brain tumor detection can be achieved with limited data using a carefully optimized CNN. Future work will expand datasets and integrate explainable AI for enhanced clinical integration.
Keywords: MRI images, deep learning, medical diagnosis, computer-aided diagnosis, healthcare, Neuroimaging
Received: 27 May 2025; Accepted: 18 Aug 2025.
Copyright: © 2025 Naeem, Osman, Alsubai, Cevik, ZAIDI and Rasheed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jawad Rasheed, Istanbul Sabahattin Zaim University, Istanbul, Türkiye
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