Brain tumor classification using deep convolutional neural networks
Nurtay M. Kissina M. Tau A. Akhmetov A. Alina G. Mutovina N.
March-April 2025Institution of Russian Academy of Sciences
Computer Optics
2025#49Issue 2253 - 262 pp.
This study presents a comparative analysis of various convolutional neural network (CNN) models for brain tumor detection on MRI medical images. The primary aim was to assess the ef-fectiveness of different CNN architectures in accurately identifying brain tumors. Multiple models were trained, including a custom-designed CNN with its specific layer architecture, and models based on Transfer Learning utilizing pre-trained neural networks: ResNet-50, VGG-16, and Xception. Performance evaluation of each model in terms of accuracy metrics such as precision, recall, F1-score, and confusion matrix on a test dataset was carried out. The dataset used in this study was obtained from the openly accessible Kaggle competition Brain Tumor Detection from MRI. This dataset consisted of four classes: glioma, meningioma, no tumor (healthy), and pituitary, ensuring a balanced representation. Testing four models revealed that the custom CNN architecture, utilizing separable convolutions and batch normalization, achieved an average ROC AUC score of 0.99, outperforming the other models. Moreover, this model demonstrated an accuracy of 0.94, indicat-ing its robust performance in brain tumor classification on MRI images.
brain tumor , computer vision , convolutional neural network , deep learning , machine learning , pattern recognition , transfer learning
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Abylkas Saginov Karagandy Technical University, 56 N. Nazarbayev avenue, Karagandy, 100000, Kazakhstan
Abylkas Saginov Karagandy Technical University
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