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

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...

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Bibliographic Details
Published in:	Diagnostics (Basel) Vol. 13; no. 12; p. 2094
Main Authors:	Asiri, Abdullah A, Shaf, Ahmad, Ali, Tariq, Shakeel, Unza, Irfan, Muhammad, Mehdar, Khlood M, Halawani, Hanan Talal, Alghamdi, Ali H, Alshamrani, Abdullah Fahad A, Alqhtani, Samar M
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01-06-2023 MDPI
Subjects:	Accuracy Algorithms Brain cancer brain tumor Brain tumors Classification CT imaging Datasets Deep learning diagnosis Edema healthcare Magnetic resonance imaging Medical imaging equipment Neural networks Pituitary gland Tomography Tumors vision transformer Saudi Arabia Germany brain tumor diagnosis vision transformer healthcare
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2075-4418 2075-4418
DOI:	10.3390/diagnostics13122094