Comparative Analysis of Vision Transformers and Convolutional Neural Networks for Medical Image Classification
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916867950510080 |
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| author | Kawadkar, Kunal |
| author_facet | Kawadkar, Kunal |
| contents | The emergence of Vision Transformers (ViTs) has revolutionized computer vision, yet their effectiveness compared to traditional Convolutional Neural Networks (CNNs) in medical imaging remains under-explored. This study presents a comprehensive comparative analysis of CNN and ViT architectures across three critical medical imaging tasks: chest X-ray pneumonia detection, brain tumor classification, and skin cancer melanoma detection. We evaluated four state-of-the-art models - ResNet-50, EfficientNet-B0, ViT-Base, and DeiT-Small - across datasets totaling 8,469 medical images. Our results demonstrate task-specific model advantages: ResNet-50 achieved 98.37% accuracy on chest X-ray classification, DeiT-Small excelled at brain tumor detection with 92.16% accuracy, and EfficientNet-B0 led skin cancer classification at 81.84% accuracy. These findings provide crucial insights for practitioners selecting architectures for medical AI applications, highlighting the importance of task-specific architecture selection in clinical decision support systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_21156 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparative Analysis of Vision Transformers and Convolutional Neural Networks for Medical Image Classification Kawadkar, Kunal Image and Video Processing Computer Vision and Pattern Recognition Machine Learning I.2.10; I.4.8 The emergence of Vision Transformers (ViTs) has revolutionized computer vision, yet their effectiveness compared to traditional Convolutional Neural Networks (CNNs) in medical imaging remains under-explored. This study presents a comprehensive comparative analysis of CNN and ViT architectures across three critical medical imaging tasks: chest X-ray pneumonia detection, brain tumor classification, and skin cancer melanoma detection. We evaluated four state-of-the-art models - ResNet-50, EfficientNet-B0, ViT-Base, and DeiT-Small - across datasets totaling 8,469 medical images. Our results demonstrate task-specific model advantages: ResNet-50 achieved 98.37% accuracy on chest X-ray classification, DeiT-Small excelled at brain tumor detection with 92.16% accuracy, and EfficientNet-B0 led skin cancer classification at 81.84% accuracy. These findings provide crucial insights for practitioners selecting architectures for medical AI applications, highlighting the importance of task-specific architecture selection in clinical decision support systems. |
| title | Comparative Analysis of Vision Transformers and Convolutional Neural Networks for Medical Image Classification |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning I.2.10; I.4.8 |
| url | https://arxiv.org/abs/2507.21156 |