A Hybrid Learning-Architecture for Improved Brain Tumor Recognition

• Published in: Algorithms Vol. 17; no. 6; p. 221
• Main Authors: Dixon, Jose, Akinniyi, Oluwatunmise, Abdelhamid, Abeer, Saleh, Gehad A., Rahman, Md Mahmudur, Khalifa, Fahmi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Ablation; Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Biopsy; Brain; Brain cancer; brain tumor; Brain tumors; Classification; Classifiers; Data augmentation; Datasets; deep learning; Diagnostic systems; Ensemble learning; Feature extraction; feature fusion; Glioma; Machine learning; Magnetic resonance imaging; Medical imaging; Medical imaging equipment; Neural networks; Optimization techniques; Pituitary gland; Support vector machines; Tumors; vision transformer; Wavelet transforms; Taiwan
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a17060221

The accurate classification of brain tumors is an important step for early intervention. Artificial intelligence (AI)-based diagnostic systems have been utilized in recent years to help automate the process and provide more objective and faster diagnosis. This work introduces an enhanced AI-based architecture for improved brain tumor classification. We introduce a hybrid architecture that integrates vision transformer (ViT) and deep neural networks to create an ensemble classifier, resulting in a more robust brain tumor classification framework. The analysis pipeline begins with preprocessing and data normalization, followed by extracting three types of MRI-derived information-rich features. The latter included higher-order texture and structural feature sets to harness the spatial interactions between image intensities, which were derived using Haralick features and local binary patterns. Additionally, local deeper features of the brain images are extracted using an optimized convolutional neural networks (CNN) architecture. Finally, ViT-derived features are also integrated due to their ability to handle dependencies across larger distances while being less sensitive to data augmentation. The extracted features are then weighted, fused, and fed to a machine learning classifier for the final classification of brain MRIs. The proposed weighted ensemble architecture has been evaluated on publicly available and locally collected brain MRIs of four classes using various metrics. The results showed that leveraging the benefits of individual components of the proposed architecture leads to improved performance using ablation studies.