Advancing brain tumor detection: harnessing the Swin Transformer's power for accurate classification and performance analysis

• Published in: PeerJ. Computer science Vol. 10; p. e1867
• Main Authors: Asiri, Abdullah A, Shaf, Ahmad, Ali, Tariq, Pasha, Muhammad Ahmad, Khan, Aiza, Irfan, Muhammad, Alqahtani, Saeed, Alghamdi, Ahmad, Alghamdi, Ali H, Alshamrani, Abdullah Fahad A, Alelyani, Magbool, Alamri, Sultan
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 29-02-2024; PeerJ Inc
• Subjects: Artificial Intelligence; Brain; Brain tumors; Computational intelligence; Computational linguistics; Computer Vision; Data Mining and Machine Learning; Electric transformers; Gliomas; Language processing; Medical image analysis; Medical imaging equipment; Natural language interfaces; Neural Networks; Swin transformer; Transformer; Tumor; Brain; Swin transformer; Vision transformer; Transformer; Medical image analysis; Tumor; Digital health care; Computational intelligence
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1867

The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach handles the classification of brain tumors across four distinct categories: glioma, meningioma, non-tumor, and pituitary, leveraging a dataset comprising 2,870 images. Employing the Swin Transformer architecture, our method intricately integrates a multifaceted pipeline encompassing sophisticated preprocessing, intricate feature extraction mechanisms, and a highly nuanced classification framework. Utilizing 21 matrices for performance evaluation across all four classes, these matrices provide a detailed insight into the model's behavior throughout the learning process, furthermore showcasing a graphical representation of confusion matrix, training and validation loss and accuracy. The standout performance parameter, accuracy, stands at an impressive 97%. This achievement outperforms established models like CNN, DCNN, ViT, and their variants in brain tumor classification. Our methodology's robustness and exceptional accuracy showcase its potential as a pioneering model in this domain, promising substantial advancements in accurate tumor identification and classification, thereby contributing significantly to the landscape of medical image analysis.