Article · Ilmiy axborotnoma/Scientific Journal of Samarkand University · July 1, 2026

A CNN–TRANSFORMER HYBRID MODEL FOR DETECTING AND CLASSIFYING BRAIN TUMORS BASED ON MRI IMAGES

D. T. Muhamediyeva, Narzillo Mamatov, A. F. Isoqov, I.J. Juraev, Sh.E. Jovliboyev

Abstract

This paper examines the problem of automatically detecting and classifying brain tumors based on magnetic resonance imaging (MRI) of the brain. The study analyzes the existing shortcomings of traditional machine learning and deep learning approaches, in particular their limitation to local features and their insufficient consideration of global spatial dependencies. To address this problem, a hybrid model combining convolutional neural networks with the self-attention mechanism of the Transformer architecture is proposed. The model enables the joint learning of local and global image features. Experiments were conducted on a four-class (glioma, meningioma, no tumor, and pituitary tumor) MRI image dataset. The results demonstrated the stable performance of the proposed model, medium-to-high accuracy, and good generalization ability. The computational complexity and stability of the model were also theoretically substantiated.

Brain Tumor Detection and ClassificationAI in cancer detectionGlioma Diagnosis and Treatmentbrain tumor detectionbrain tumor classificationMRI images

A CNN–TRANSFORMER HYBRID MODEL FOR DETECTING AND CLASSIFYING BRAIN TUMORS BASED ON MRI IMAGES

Abstract

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