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COMPARATIVE EFFICIENCY ANALYSIS OF CNN AND VIT MODELS IN BRAIN CANCER DETECTION BASED ON MRI IMAGES

Akhram Khasanovich NishanovTashkent University of Information Technologies, Doctor of Technical Sciences (DSc), profFazil Makhsetovich ZaripovTashkent University of Information Technologies, Doctor of Philosophy (PhD) in Technical SciencesBakhriddin Panji ugli KambarovPhD student at Tashkent State University of EconomicsNodirbek Sobirovich JurakulovPhD student at Tashkent University of Information TechnologiesAbdul-Aziz Karamatdinovich MakhamatdinovPhD student of Tashkent State University of Economics

Zenodo (CERN European Organization for Nuclear Research)repository2026en

ABI

Аннотация

In this study, the task of classifying brain tumors based on magnetic resonance imaging was analyzed using deep learning approaches. The study compared convolutional neural networks and transformer-based models, including the Vision Transformer and DEiT architectures. The experiments were conducted on an open "Brain Tumor MRI Dataset" dataset, and the models were evaluated based on accuracy, precision, recall, and F1-score metrics. According to the results, the DEiT model demonstrated the highest efficiency, achieving 98.04% validation and 94.44% testing accuracy. The results obtained showed the superiority of transformer models due to their ability to effectively study global properties and are of great importance for the development of automated diagnostic systems based on MRI images.

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Мавзулар

Brain Tumor Detection and Classification Advanced Neural Network Applications Glioma Diagnosis and Treatment

Идентификаторлар

DOI: 10.5281/zenodo.20596769

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