Exploring the Power of Deep Learning: Fine-Tuned Vision Transformer for Accurate and Efficient Brain Tumor Detection in MRI Scans.

Autor: Asiri AA; Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia., Shaf A; Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan., Ali T; Department of Computer Science, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan., Shakeel U; Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan., Irfan M; Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia., Mehdar KM; Anatomy Department, Medicine College, Najran University, Najran 61441, Saudi Arabia., Halawani HT; Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia., Alghamdi AH; Department of Radiological Sciences, Faculty of Applied Medical Sciences, The University of Tabuk, Tabuk 47512, Saudi Arabia., Alshamrani AFA; Department of Diagnostic Radiology Technology, College of Applied Medical Sciences, Taibah University, Madinah 41477, Saudi Arabia., Alqhtani SM; Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia.
Jazyk: angličtina
Zdroj: Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2023 Jun 16; Vol. 13 (12). Date of Electronic Publication: 2023 Jun 16.
DOI: 10.3390/diagnostics13122094
Abstrakt: A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. There are several imaging techniques used for brain tumor detection. Among these techniques, the most common are MRI and CT scans. To overcome the limitations associated with these traditional techniques, computer-aided analysis of brain images has gained attention in recent years as a promising approach for accurate and reliable brain tumor detection. In this study, we proposed a fine-tuned vision transformer model that uses advanced image processing and deep learning techniques to accurately identify the presence of brain tumors in the input data images. The proposed model FT-ViT involves several stages, including the processing of data, patch processing, concatenation, feature selection and learning, and fine tuning. Upon training the model on the CE-MRI dataset containing 5712 brain tumor images, the model could accurately identify the tumors. The FT-Vit model achieved an accuracy of 98.13%. The proposed method offers high accuracy and can significantly reduce the workload of radiologists, making it a practical approach in medical science. However, further research can be conducted to diagnose more complex and rare types of tumors with more accuracy and reliability.
Competing Interests: The authors declare no conflict of interest.
Databáze: MEDLINE
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