This study evaluates eight fine-tuning strategies for transfer learning in medical image classification across five domains: X-ray, MRI, histology, dermoscopy, and endoscopy. The research compares the effectiveness of various fine-tuning methods, including full fine-tuning, linear probing, gradual unfreezing, and regularization-based approaches, using three CNN architectures (ResNet-50, DenseNet-121, and VGG-19). The results show that strategies like combining linear probing with full fine-tuning improved performance in over 50% of cases, while Auto-RGN enhanced performance by up to 11% for specific modalities. DenseNet-121 showed more significant benefits from alternative fine-tuning approaches compared to traditional full fine-tuning. The study highlights the importance of selecting appropriate fine-tuning strategies based on the architecture and medical imaging type, and suggests that further research into advanced architectures and fine-tuning methods could improve medical image analysis. The findings emphasize the need for careful consideration of domain-specific characteristics when applying transfer learning in medical imaging.This study evaluates eight fine-tuning strategies for transfer learning in medical image classification across five domains: X-ray, MRI, histology, dermoscopy, and endoscopy. The research compares the effectiveness of various fine-tuning methods, including full fine-tuning, linear probing, gradual unfreezing, and regularization-based approaches, using three CNN architectures (ResNet-50, DenseNet-121, and VGG-19). The results show that strategies like combining linear probing with full fine-tuning improved performance in over 50% of cases, while Auto-RGN enhanced performance by up to 11% for specific modalities. DenseNet-121 showed more significant benefits from alternative fine-tuning approaches compared to traditional full fine-tuning. The study highlights the importance of selecting appropriate fine-tuning strategies based on the architecture and medical imaging type, and suggests that further research into advanced architectures and fine-tuning methods could improve medical image analysis. The findings emphasize the need for careful consideration of domain-specific characteristics when applying transfer learning in medical imaging.