Learning architecture for brain tumor classification based on deep convolutional neural network: Classic and ResNet50

Doheir, Mohamed A. S. and Mohd Yaacob, Noorayisahbe and Ali, Rabei Raad and Alqaryouti, Marwan Harb and Sadeq, Ala Eddin and Iqtait, Musab and Rachmawanto, Eko Hari and Sari, Christy Atika and Yaacob, Siti Salwani (2025) Learning architecture for brain tumor classification based on deep convolutional neural network: Classic and ResNet50. Diagnostics, 15 (5). pp. 1-14. ISSN 2075-4418

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Abstract

Accurate classification of brain tumors in medical images is vital for effective diagnosis and treatment planning, which improves the patient’s survival rate. In this paper, we investigate the application of Convolutional Neural Networks (CNN) as a powerful tool for enhancing diagnostic accuracy using a Magnetic Resonance Imaging (MRI) dataset. Method: This study investigates the application of CNNs for brain tumor classification using a dataset of Magnetic Resonance Imaging (MRI) with a resolution of 200 × 200 × 1. The dataset is pre-processed and categorized into three types of tumors: Glioma, Meningioma, and Pituitary. The CNN models, including the Classic layer architecture and the ResNet50 architecture, are trained and evaluated using an 80:20 training-testing split. Results: The results reveal that both architectures accurately classify brain tumors. Classic layer architecture achieves an accuracy of 94.55%, while the ResNet50 architecture surpasses it with an accuracy of 99.88%. Compared to previous studies and 99.34%, our approach offers higher precision and reliability, demonstrating the effectiveness of ResNet50 in capturing complex features. Conclusions: The study concludes that CNNs, particularly the ResNet50 architecture, exhibit effectiveness in classifying brain tumors and hold significant potential in aiding medical professionals in accurate diagnosis and treatment planning. These advancements aim to further enhance the performance and practicality of CNN-based brain tumor classification systems, ultimately benefiting healthcare professionals and patients. For future research, exploring transfer learning techniques could be beneficial. By leveraging pre-trained models on large-scale datasets, researchers can utilize knowledge from other domains to improve brain tumor classification tasks, particularly in scenarios with limited annotated data.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, Convolutional Neural Networks, ResNet-50, Image classification, Magnetic resonance imaging
Divisions:	Faculty of Technology Management and Technopreneurship
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	30 Dec 2025 06:28
Last Modified:	30 Dec 2025 06:28
URI:	http://eprints.utem.edu.my/id/eprint/29344
Statistic Details:	View Download Statistic

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