A Unified Deep Learning Framework for Cross-Domain Transfer Learning in Medical Imaging, Energy Forecasting, and Financial Risk Assessment.

Narcisa Zlatan

A Unified Deep Learning Framework for Cross-Domain Transfer Learning in Medical Imaging, Energy Forecasting, and Financial Risk Assessment.

Narcisa Zlatan
Volume 5

Abstract

Cross-domain transfer learning has become an important research direction for developing intelligent systems that can generalize across heterogeneous data environments, particularly in domains where labeled data are scarce, expensive, imbalanced, or highly specialized. Medical imaging, energy forecasting, and financial risk assessment represent three critical application areas with distinct data structures, including visual diagnostic images, temporal energy demand patterns, and multivariate financial risk indicators. Despite their differences, these domains share common modeling challenges, such as distribution shift, limited labeled samples, nonlinear feature interactions, noise, uncertainty, and the need for interpretable decision support. This paper proposes a unified deep learning framework for cross-domain transfer learning across medical imaging, energy forecasting, and financial risk assessment. The proposed framework combines convolutional neural networks, recurrent neural networks, transformer-based architectures, autoencoders, and attention mechanisms within a modular architecture capable of extracting transferable representations from image, time-series, and tabular data. Domain adaptation layers, shared feature encoders, task-specific prediction heads, and fine-tuning strategies are incorporated to enable knowledge transfer from source domains with abundant data to target domains with limited labeled observations. The framework was evaluated using representative datasets from retinal disease detection and brain tumor classification, short-term electricity load and renewable energy forecasting, and credit default and systemic financial risk prediction. A comprehensive preprocessing pipeline was applied for each domain, including image enhancement and augmentation for medical imaging, normalization and sliding-window construction for energy forecasting, and missing value treatment, feature engineering, and imbalance handling for financial risk datasets. Experimental results demonstrate that the proposed unified framework improves predictive performance compared with domain-specific standalone models and conventional transfer learning baselines across all evaluated tasks. The framework achieves higher classification accuracy, sensitivity, specificity, F1-score, AUC-ROC, and lower forecasting errors measured by RMSE, MAE, and MAPE, while requiring fewer labeled target-domain samples. Furthermore, attention visualization, Grad-CAM, SHAP analysis, and latent feature interpretation were integrated to enhance model transparency and provide domain-relevant explanations for clinical, energy, and financial decision-makers. The findings indicate that unified cross-domain transfer learning can provide a scalable, adaptive, and data-efficient solution for complex real-world prediction problems, supporting reliable decision-making in healthcare diagnostics, smart energy management, and financial risk monitoring.

Keywords

References

[1] Alhussan, A. A., Khafaga, D. S., Abotaleb, M., Mishra, P., & El -Kenawy, E. S. M. (2024). Global Potato Production Forecasting Based
[2] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
[3] Mishra, P., Alhussan, A. A., Khafaga, D. S., Lal, P., Ray, S., Abotaleb, M., ... & El -Kenawy, E. S. M. (2024). Forecasting production of
[4] El-Kenawy, E. S. M., Mirjalili, S., Abdelhamid, A. A., Ibrahim, A., Khodadadi, N., & Eid, M. M. (2022). Meta -heuristic optimization
[5] Abdelhamid, A. A., El -Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Khafaga, D. S., Alharbi, A. H., ... & Saber, M. (2022).
[6] Alharbi, A. H., Towfek, S. K., Abdelhamid, A. A., Ibrahim, A., Eid, M. M., & Khafaga, D. S. & Saber, M.(2023). Diagnosis of
[7] Alhussan, A. A., & Towfek, S. K. (2024). 5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algori thm.
[8] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
[9] Abdelhamid, A. A., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Eed, M. (2024). Potato harvesting pred iction
[10] Eed, M., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Arnous, R. (2024). Potato Consumption Forecastin g
[11] Mahmood, S., Sun, H., Iqbal, A., Alhussan, A. A., & El -kenawy, E. S. M. (2024). Green finance, sustainable infrastructure, and green
[12] El-Kenawy, E. S. M., Alhussan, A. A., Khodadadi, N., Mirjalili, S., & Eid, M. M. (2024). Predicting potato crop yield with machi ne
[13] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
[14] Ghasemi, M., Khodadadi, N., Trojovský, P., Li, L., Mansor, Z., Abualigah, L., ... & El -Kenawy, E. S. M. (2025). Kirchhoff’s law
[15] Yassen, M. A., El -Kenawy, E. S. M., Abdel -Fattah, M. G., Ismail, I., & Mostafa, H. E. D. S. (2025). Explainable artificial intelligence
[16] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
[17] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
[18] El-kenawy, E. S. M., Alhussan, A. A., Mattar, E. A., & Radwan, M. (2026). Feature selection and hyperparameter tuning in transfo rmer -
[19] Chen, L., Xu, C., Lim, W. H., Sharma, A., Tiang, S. S., Chong, K. S., ... & Khafaga, D. S. (2025). Transparent and reliable c onstruction
[20] Khodadadi, N., Towfek, S. K., Zaki, A. M., Alharbi, A. H., Khodadadi, E., Khafaga, D. S., ... & Eid, M. M. (2025). Predicting
[21] Alhussan, A. A., El -Kenawy, E. S. M., Khafaga, D. S., Alharbi, A. H., & Eid, M. M. (2025). Groundwater resource prediction and
[22] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
[23] Lopes, J. M., Pinho, C. S., Martins, R. C., & Bandeira, T. (2026). Sustainability Gets Smarter: Competitive Pressure and AI L eading the
[24] Radwan, M., Ibrahim, A., Abdelsalam, M. M., Alhussan, A. A., Mattar, E. A., & El -Kenawy, E. S. M. (2026). Optimizing solar and wind
[25] Alhussan , A. A., El -Kenawy, E. S. M., Eid, M. M., & Khodadadi, N. (2026). Hybrid Al -Biruni and Puma Optimization (BERPO) for
[26] El-Kenawy, E. S. M., Ibrahim, A., Alhussan, A. A., Khafaga, D. S., Ahmed, A. E., & Eid, M. M. (2026). Smart city electricity loa d
[27] El-Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Zaki, A. M., Ibrahim, A., Alhussan, A. A., ... & Eid, M. M. (2026). Glider sn ake
[28] Mekaret, F., Rabehi, A., Zebentout, B., Tizi, S., Douara, A., Bellucci, S., ... & Alhussan, A. A. (2024). A comparative study of Schottky
[29] KOUADRI, Ali, RABEHI, Abdelhalim, BENZIANE, Ali, et al. A Robust Multi -Transform Watermarking Scheme for Medical Images
[30] Tibermacine, I. E., Russo, S., Scarano, G., Tedesco, G., Rabehi, A., Alhussan, A. A., ... & Napoli, C. (2025). Conditional VA E for
[31] Kouadri, A., Benziane, A., Rabehi, A., Rabehi, A., Alhussan, A. A., Khafaga, D. S., & El -Kenawy, E. S. M. (2025). A novel hybrid
[32] Russo, S., Tibermacine, I. E., Randieri, C., Rabehi, A., Alharbi, A. H., El -Kenawy, E. S. M., & Napoli, C. (2025). Exploiting facial
[33] Bentegri, H., Rabehi, M., Kherfane, S., Nahool, T. A., Rabehi, A., Guermoui, M., ... & El -Kenawy, E. S. M. (2025). Assessment of
[34] Tibermacine, I. E., Russo, S., Citeroni, F., Mancini, G., Rabehi, A., Alharbi, A. H., ... & Napoli, C. (2025). Adversarial de noising of EEG
[35] Ouahabi, M. S., Benyounes, A., Barkat, S., Ihammouchen, S., Rekioua, T., Rabehi, A., ... & Alharbi, A. H. (2025). Real -time sensor fault
[36] Belaid, A., Guermoui, M., Khelifi, R., Arrif, T., Chekifi, T., Rabehi, A., ... & Alhussan, A. A. (2024). Assessing suitable a reas for PV
[37] Mehallou, A., M’hamdi, B., Amari, A., Teguar, M., Rabehi, A., Guermoui, M., ... & Khafaga, D. S. (2025). Optimal multiobjecti ve
[38] Rabehi, A., El -Hadi, M., Benmahmoud, S., Rabehi, A., Alharbi, A. H., & El -Kenawy, E. S. M. (2025). SOCA -CFAR Processor in A
[39] Bakria, D., Beladel, A., Korich, B., Teta, A., Mohammedi, R. D., Laouid, A. A., ... & El -kenawy, E. S. (2025). A novel enhanced Grey

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