Evaluating The Impact Of Transfer Learning Vs Fine-Tuning In Medical Image Segmentation
Ch.G.V.N. Prasad, K. Kavya, Manasa G
This study investigates the impact of transfer learning and fine-tuning on the performance of medical
image segmentation tasks across different imaging modalities, including MRI, CT, and ultrasound.
Leveraging pre-trained models such as U-Net, ResNet, and VGG16, we conduct extensive experiments
to compare the efficacy of transfer learning against fine-tuning in segmenting brain tumors, livers,
and kidneys. Our results demonstrate that while transfer learning provides a strong baseline for
medical image analysis, fine-tuning consistently enhances model performance, leading to significant
improvements in Dice coefficient, Intersection over Union (IoU), precision, and recall across all
datasets. These findings underscore the importance of fine-tuning in adapting pre-trained models to
the unique challenges of medical imaging, offering valuable insights for developing more accurate and
reliable segmentation models in clinical practice.
