Authors:
Safa Jraba
1
;
Mohamed Elleuch
2
;
Hela Ltifi
3
and
Monji Kherallah
4
Affiliations:
1
National School of Electronics and Telecommunications (ENETCom), University of Sfax, Tunisia
;
2
National School of Computer Science (ENSI), University of Manouba, Tunisia
;
3
Faculty of Sciences and Techniques of Sidi Bouzid, University of Kairouan, Tunisia
;
4
Faculty of Sciences, University of Sfax, Tunisia
Keyword(s):
Brain Tumor, Deep Learning, Diagnosis, EfficientNet, ViT, Medical Imaging, Classification, SVM.
Abstract:
Brain tumors are irregular cell mixtures existing within the brain or central spinal canal. They could be cancerous or benign. The likelihood of the best possible prognosis and therapy increases with the speed and accuracy of detection. This work provides a method for detecting brain tumors that combines the capabilities of vision transformers and CNNs. In contrast to other studies that primarily relied on standalone CNN or ViT architectures, our method uniquely integrates these models with a Support Vector Machine classifier for the improvement of accuracy and robustness in medical image classification. While the ViT makes it possible to combine CNN and ViT to improve the accuracy of medical imaging of the disease, the CNN extracts hierarchical features. In-depth analyses of benchmark datasets pertaining to imaging modalities and clinical perspectives were conducted. According to the experimental findings, ViT and EfficientNet identified tumors with an accuracy of 98%, while the gre
atest reported accuracy of 98.3% was obtained when ViT was combined with an SVM classifier. Our findings suggest that our method may improve brain tumor detection methods.
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