This study investigates the effectiveness of various fine-tuning strategies for adapting pre-trained models to specialized medical imaging tasks. The research evaluates eight fine-tuning methods across five medical imaging domains: X-ray, MRI, histology, dermoscopy, and endoscopic surgery. The methods include standard techniques such as full fine-tuning and linear probing, as well as more advanced approaches like gradual unfreezing layers, regularization, and adaptive learning rates. Three well-established CNN architectures—ResNet-50, DenseNet-121, and VGG-19—are used to cover a range of learning and feature extraction scenarios. The results show that the efficacy of these fine-tuning methods varies significantly depending on both the architecture and the medical imaging type. Notably, combining Linear Probing with Full Fine-tuning resulted in notable improvements in over 50% of the evaluated cases, demonstrating general effectiveness across medical domains. Additionally, Auto-RGN, which dynamically adjusts learning rates, led to performance enhancements of up to 11% for specific modalities. The study also highlights that the DenseNet architecture showed more pronounced benefits from alternative fine-tuning approaches compared to traditional full fine-tuning. This work provides valuable insights for optimizing pre-trained models in medical image analysis and suggests potential directions for future research into more advanced architectures and fine-tuning methods.This study investigates the effectiveness of various fine-tuning strategies for adapting pre-trained models to specialized medical imaging tasks. The research evaluates eight fine-tuning methods across five medical imaging domains: X-ray, MRI, histology, dermoscopy, and endoscopic surgery. The methods include standard techniques such as full fine-tuning and linear probing, as well as more advanced approaches like gradual unfreezing layers, regularization, and adaptive learning rates. Three well-established CNN architectures—ResNet-50, DenseNet-121, and VGG-19—are used to cover a range of learning and feature extraction scenarios. The results show that the efficacy of these fine-tuning methods varies significantly depending on both the architecture and the medical imaging type. Notably, combining Linear Probing with Full Fine-tuning resulted in notable improvements in over 50% of the evaluated cases, demonstrating general effectiveness across medical domains. Additionally, Auto-RGN, which dynamically adjusts learning rates, led to performance enhancements of up to 11% for specific modalities. The study also highlights that the DenseNet architecture showed more pronounced benefits from alternative fine-tuning approaches compared to traditional full fine-tuning. This work provides valuable insights for optimizing pre-trained models in medical image analysis and suggests potential directions for future research into more advanced architectures and fine-tuning methods.