Advancing Brain Tumor Classification through Fine-Tuned Vision Transformers: A Comparative Study of Pre-Trained Models
Author:
Asiri Abdullah A.1, Shaf Ahmad2ORCID, Ali Tariq2, Pasha Muhammad Ahmad2, Aamir Muhammad2ORCID, Irfan Muhammad3ORCID, Alqahtani Saeed1, Alghamdi Ahmad Joman4ORCID, Alghamdi Ali H.5, Alshamrani Abdullah Fahad A.6ORCID, Alelyani Magbool7ORCID, Alamri Sultan4
Affiliation:
1. Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia 2. Department of Computer Science, COMSATS University Islamabad Sahiwal Campus, Sahiwal 57000, Pakistan 3. Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia 4. Radiological Sciences Department, College of Applied Medical Sciences, Taif University, Taif 21944, Saudi Arabia 5. Department of Radiological Sciences, Faculty of Applied Medical Sciences, The University of Tabuk, Tabuk 47512, Saudi Arabia 6. Department of Diagnostic Radiology Technology, College of Applied Medical Sciences, Taibah University, Madinah 41477, Saudi Arabia 7. Department of Radiological Sciences, College of Applied Medical Science, King Khalid University, Abha 61421, Saudi Arabia
Abstract
This paper presents a comprehensive study on the classification of brain tumor images using five pre-trained vision transformer (ViT) models, namely R50-ViT-l16, ViT-l16, ViT-l32, ViT-b16, and ViT-b32, employing a fine-tuning approach. The objective of this study is to advance the state-of-the-art in brain tumor classification by harnessing the power of these advanced models. The dataset utilized for experimentation consists of a total of 4855 images in the training set and 857 images in the testing set, encompassing four distinct tumor classes. The performance evaluation of each model is conducted through an extensive analysis encompassing precision, recall, F1-score, accuracy, and confusion matrix metrics. Among the models assessed, ViT-b32 demonstrates exceptional performance, achieving a high accuracy of 98.24% in accurately classifying brain tumor images. Notably, the obtained results outperform existing methodologies, showcasing the efficacy of the proposed approach. The contributions of this research extend beyond conventional methods, as it not only employs cutting-edge ViT models but also surpasses the performance of existing approaches for brain tumor image classification. This study not only demonstrates the potential of ViT models in medical image analysis but also provides a benchmark for future research in the field of brain tumor classification.
Funder
Deanship of Scientific Research, Najran University Kingdom of Saudi Arabia
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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