Open Access

ARTICLE

A Novel Dynamic Residual Self-Attention Transfer Adaptive Learning Fusion Approach for Brain Tumor Diagnosis

Tawfeeq Shawly¹, Ahmed A. Alsheikhy^2,*

1 Electrical Engineering Department, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah, 21981, Saudi Arabia
2 Electrical Engineering Department, College of Engineering, Northern Border University, Arar, 91431, Saudi Arabia

* Corresponding Author: Ahmed A. Alsheikhy. Email:

(This article belongs to the Special Issue: Advancements in Machine Learning and Artificial Intelligence for Pattern Detection and Predictive Analytics in Healthcare)

Computers, Materials & Continua 2025, 82(3), 4161-4179. https://doi.org/10.32604/cmc.2025.061497

Received 26 November 2024; Accepted 31 January 2025; Issue published 06 March 2025

Abstract

A healthy brain is vital to every person since the brain controls every movement and emotion. Sometimes, some brain cells grow unexpectedly to be uncontrollable and cancerous. These cancerous cells are called brain tumors. For diagnosed patients, their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans. Nowadays, Physicians and radiologists rely on Magnetic Resonance Imaging (MRI) pictures for their clinical evaluations of brain tumors. These evaluations are time-consuming, expensive, and require expertise with high skills to provide an accurate diagnosis. Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy. Due to their accuracy, some of these solutions depend on deep-learning (DL) methodologies. These techniques have become important due to their roles in the diagnosis process, which includes identification and classification. Therefore, there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors. The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis. The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach (NDDRSATALFA), carried over two implemented deep-learning networks: VGG19 and UNET to identify and classify brain tumors. In addition, this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks. The presented framework is trained, validated, and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes, which are glioma, meningioma, and pituitary. The proposed framework yielded remarkable findings on variously evaluated performance indicators: 99.32% accuracy, 98.74% sensitivity, 98.89% specificity, 99.01% Dice, 98.93% Area Under the Curve (AUC), and 99.81% F1-score. In addition, a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis, NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors. Moreover, this framework can be applied by healthcare providers to assist radiologists, pathologists, and physicians in their evaluations. The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.

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