Transformation of Medical Images with Transfer Learning: Trends and Challenges

The lack of labeled data and the requirement for high accuracy in diagnosing medical disorders are the main obstacles to medical image categorization. Medical image labeling necessitates specialized knowledge, which is costly and timeconsuming, leading to small datasets. The development of precise diagnostic tools is hampered by the difficulty of training deep learning models from scratch due to the absence of adequate labeled data. This issue is further complicated by the fact that medical frequently shows substantial variability because of variations in imaging modalities, patient demographics, and diseases development. By using pre-trained models from extensive datasets and optimizing them for particular medical imaging applications, transfer learning provides a viable solution. This paper examines potency of transfer learning in healthcare to enhance accuracy with a small dataset. Transfer learning is used to refine a previously trained convolutional neural network (CNN), such as ResNet or EfficientNet, on a medical imaging dataset. The performance of transfer learning and convolutional deep learning models generated from scratch is compared in the study. The efficacy of the model has been evaluated using metrics like accuracy, precision, recall, and F1-score. This study shows that transfer learning is a promising approach for real-world deployment in resource-constrained contexts since it not only increases classification accuracy but also lowers computational costs. Future studies could investigate domain- specific previously trained and hybrid techniques to further improve classification accuracy.