<![CDATA[This literature review delves into the recent advancements in transfer learning, examining its applications and enhancements across diverse domains. Focused on the conclusions drawn from various studies, the review highlights the evolution of transfer learning in three key areas: text analysis, image processing, and health data analytics. In text analysis, innovations such as BERT, CNN-BiLSTM, AdapterFusion, and T-BERT Framework showcase the ongoing efforts to improve efficiency and adaptability in understanding complex natural language tasks. Similarly, in image processing, the review emphasizes the varied use of pre-trained models, feature extraction techniques, and diversified datasets, leading to enhanced performance in tasks like image classification, object detection, and facial recognition. Furthermore, the application of transfer learning in health data analytics, particularly with CNN models like AlexNet, ResNet, GoogLeNet, and EfficientNet, reflects significant progress in tasks such as MRI brain image classification, skin lesion analysis, brain tumor detection, and other medical image analyses. The advantages of transfer learning, including consistent performance improvement and computational efficiency through the use of pre-trained models, are discussed. However, challenges such as overfitting in specific contexts and the need for careful adaptation in medical data analytics are acknowledged. The review concludes with recommendations for future research directions, urging a focus on improving domain-specific adaptation, exploring multi-modal model integration, enhancing transfer learning for limited health datasets, and investigating its potential for multi-task applications in text, image, and health data analytics. The comprehensive insights provided in this review contribute to the understanding and advancement of transfer learning in the current landscape of artificial intelligence research.]]>
Copyrights © 2025
