Transfer Learning Advancements: A Comprehensive Literature Review on Text Analysis, Image Processing, and Health Data Analytics

Andi Wafda

doi:10.54065/artificial.544

Authors

Andi Wafda Universitas Islam Indonesia

DOI:

https://doi.org/10.54065/artificial.544

Keywords:

Transfer Learning, Fine-Tuning, Model Pre-Trained, BERT, CNN

Abstract

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.

References

Alaparthi, S., & Mishra, M. (2021). BERT: A sentiment analysis odyssey. Journal of Marketing Analytics, 9(2), 118-126.

Aslan, M. F., Unlersen, M. F., Sabanci, K., & Durdu, A. (2021). CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection. Applied Soft Computing, 98, 106912.

Bansal, M., Kumar, M., Sachdeva, M., & Mittal, A. (2021). Transfer learning for image classification using VGG19: Caltech-101 image data set. Journal of ambient intelligence and humanized computing, 1-12.

Baykal, E., Dogan, H., Ercin, M. E., Ersoz, S., & Ekinci, M. (2020). Transfer learning with pre-trained deep convolutional neural networks for serous cell classification. Multimedia Tools and Applications, 79, 15593-15611.

Chakraborty, S., Uzkent, B., Ayush, K., Tanmay, K., Sheehan, E., & Ermon, S. (2022). Efficient conditional pre-training for transfer learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4241-4250).

Chen, Y., Qin, X., Wang, J., Yu, C., & Gao, W. (2020). Fedhealth: A federated transfer learning framework for wearable healthcare. IEEE Intelligent Systems, 35(4), 83-93.

Deniz, E., ?engür, A., Kadiro?lu, Z., Guo, Y., Bajaj, V., & Budak, Ü. (2018). Transfer learning based histopathologic image classification for breast cancer detection. Health information science and systems, 6, 1-7.

Espejo-Garcia, B., Mylonas, N., Athanasakos, L., Fountas, S., & Vasilakoglou, I. (2020). Towards weeds identification assistance through transfer learning. Computers and Electronics in Agriculture, 171, 105306.

Han, D., Liu, Q., & Fan, W. (2018). A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications, 95, 43-56.

Kaur, T., & Gandhi, T. K. (2020). Deep convolutional neural networks with transfer learning for automated brain image classification. Machine vision and applications, 31(3), 20.

Mahbod, A., Schaefer, G., Wang, C., Dorffner, G., Ecker, R., & Ellinger, I. (2020). Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification. Computer methods and programs in biomedicine, 193, 105475.

Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information systems and e-business management, 16, 547-578.

Pathak, Y., Shukla, P. K., Tiwari, A., Stalin, S., & Singh, S. (2022). Deep transfer learning based classification model for COVID-19 disease. Irbm, 43(2), 87-92.

Pfeiffer, J., Kamath, A., Rücklé, A., Cho, K., & Gurevych, I. (2020). AdapterFusion: Non-destructive task composition for transfer learning. arXiv preprint arXiv:2005.00247.

Prottasha, N. J., Sami, A. A., Kowsher, M., Murad, S. A., Bairagi, A. K., Masud, M., & Baz, M. (2022). Transfer learning for sentiment analysis using BERT based supervised fine-tuning. Sensors, 22(11), 4157.

Qasim, R., Bangyal, W. H., Alqarni, M. A., & Ali Almazroi, A. (2022). A fine-tuned BERT-based transfer learning approach for text classification. Journal of healthcare engineering, 2022.

Rehman, A., Naz, S., Razzak, M. I., Akram, F., & Imran, M. (2020). A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits, Systems, and Signal Processing, 39, 757-775.

Sudhakar & Kumar, S. (2021). MCFT-CNN: Malware classification with fine-tune convolution neural networks using traditional and transfer learning in Internet of Things. Future Generation Computer Systems, 125, 334-351.

Wang, C., Chen, D., Hao, L., Liu, X., Zeng, Y., Chen, J., & Zhang, G. (2019). Pulmonary image classification based on inception-v3 transfer learning model. IEEE Access, 7, 146533-146541.

Wang, J., Chen, Y., Feng, W., Yu, H., Huang, M., & Yang, Q. (2020). Transfer learning with dynamic distribution adaptation. ACM Transactions on Intelligent Systems and Technology (TIST), 11(1), 1-25.

Wang, S., Nepal, S., Rudolph, C., Grobler, M., Chen, S., & Chen, T. (2020). Backdoor attacks against transfer learning with pre-trained deep learning models. IEEE Transactions on Services Computing, 15(3), 1526-1539.