Open Access

ARTICLE

Enhanced Classification of Brain Tumor Types Using Multi-Head Self-Attention and ResNeXt CNN

Muhammad Naeem^*, Abdul Majid

Pattern Recognition Lab, Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, 45650, Islamabad, Pakistan

* Corresponding Author: Muhammad Naeem. Email:

Journal on Artificial Intelligence 2025, 7, 115-141. https://doi.org/10.32604/jai.2025.062446

Received 18 December 2024; Accepted 29 April 2025; Issue published 30 May 2025

Abstract

Brain tumor identification is a challenging task in neuro-oncology. The brain’s complex anatomy makes it a crucial part of the central nervous system. Accurate tumor classification is crucial for clinical diagnosis and treatment planning. This research presents a significant advancement in the multi-classification of brain tumors. This paper proposed a novel architecture that integrates Enhanced ResNeXt 101_32×8d, a Convolutional Neural Network (CNN) with a multi-head self-attention (MHSA) mechanism. This combination harnesses the strengths of the feature extraction, feature representation by CNN, and long-range dependencies by MHSA. Magnetic Resonance Imaging (MRI) datasets were employed to check the effectiveness of the proposed architecture. The first dataset (DS-1, Msoud) included four brain tumor classes, and the second dataset (DS-2) contained seven brain tumor classes. This methodology effectively distinguished various tumor classes, achieving high accuracies of 99.75% on DS-1 and 98.80% on DS-2. These impressive results indicate the superior performance and adaptability of our model for multiclass brain tumor classification. Evaluation metrics such as accuracy, precision, recall, F1 score, and ROC (receiver operating characteristic) curve were utilized to comprehensively evaluate model validity. The performance results showed that the model is well-suited for clinical applications, with reduced errors and high accuracy.

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