Comparative Analysis of Vision Transformers and Convolutional Neural Networks for Medical Image Classification
It provides insights for practitioners selecting architectures for medical AI applications, but is incremental as it applies existing methods to new data.
This study compared Vision Transformers and Convolutional Neural Networks on medical image classification tasks, finding task-specific advantages with accuracies ranging from 81.84% to 98.37% across chest X-ray, brain tumor, and skin cancer datasets.
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.