Medical imaging is a very important part of diagnosis because it helps doctors find diseases and plan treatments accurately. But for deep learning models to work well in medical picture analysis, they need a lot of different kinds of data, which is hard to find and costs a lot of money. Using pre-trained models on big datasets from similar areas, transfer learning looks like a good way to solve this problem. This paper looks into how to use transfer learning optimization methods to improve the accuracy of medical imaging diagnostics when samples are limited. We look into a number of different approaches, such as fine-tuning, feature extraction, and changes to the network design. Through real-world testing on a limited dataset, we show that transfer learning is a better way to improve model performance than starting from scratch. In addition, we look into how sample size and topic similarity affect how well transfer learning works. The results show that transfer learning could be a good way to improve the efficiency of medical imaging jobs for diagnosis, especially when there isn’t a lot of data available. Transfer learning lets you make strong and effective assessment tools even in healthcare settings with limited resources by using knowledge that is already stored in models that have already been taught.