JCSIS
019928311823 info@jcsis.org
JCSIS

Generative Adversarial Networks for Synthetic Medical Image Augmentation in Rare Disease Diagnosis.

Weiguo Gee
Volume 5

Abstract

Rare diseases collectively affect millions of individuals worldwide, yet their diagnosis remains a significant challenge due to limited clinical expertise, heterogeneous disease manifestations, and, most importantly, the scarcity of annotated medical imaging data required for training robust artificial intelligence models. In medical image analysis, deep learning algorithms often require large and diverse datasets to achieve reliable performance; however, rare disease datasets are frequently characterized by small sample sizes, severe class imbalance, privacy restrictions, and limited availability of expert annotations. These constraints increase the risk of overfitting and reduce the generalizability of diagnostic models. To address these challenges, this paper proposes a Generative Adversarial Network (GAN)-based framework for synthetic medical image augmentation aimed at improving rare disease diagnosis through the generation of realistic and clinically meaningful synthetic imaging samples.

The proposed framework employs advanced GAN architectures, including Deep Convolutional GAN (DCGAN), Conditional GAN (cGAN), Wasserstein GAN with Gradient Penalty (WGAN-GP), and StyleGAN-based models, to generate high-quality synthetic medical images that preserve disease-specific pathological characteristics. The generation process is conditioned on clinically relevant attributes such as disease subtype, anatomical region, imaging modality, and disease severity level, enabling the creation of diverse yet diagnostically consistent samples. The framework is designed to support multiple imaging modalities, including magnetic resonance imaging (MRI), computed tomography (CT), chest radiography, retinal imaging, histopathological slides, and ultrasound images associated with rare pathological conditions.

A comprehensive preprocessing pipeline is applied before image generation, including image normalization, artifact removal, contrast enhancement, spatial standardization, and annotation verification. Following synthetic image generation, quality assessment mechanisms are employed using Fréchet Inception Distance (FID), Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and expert radiologist evaluation to ensure anatomical realism and pathological fidelity. The generated synthetic images are subsequently integrated into the training datasets of downstream diagnostic models, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid deep learning architectures. To further improve data diversity, the framework combines GAN-generated samples with conventional augmentation techniques such as rotation, scaling, flipping, elastic deformation, and intensity transformation.

The proposed methodology is evaluated using publicly available and institution-specific rare disease imaging datasets under varying levels of data scarcity. Experimental performance is assessed using accuracy, sensitivity, specificity, F1-score, AUC-ROC, balanced accuracy, and cross-dataset generalization measures. Results are expected to demonstrate that GAN-based augmentation significantly enhances diagnostic performance compared with conventional augmentation strategies and training on original datasets alone. In particular, the inclusion of synthetic images improves minority-class representation, reduces model bias, increases robustness to unseen clinical cases, and enhances the detection of subtle disease-specific imaging patterns that are often underrepresented in limited datasets.

To promote transparency and clinical acceptance, explainability techniques such as Grad-CAM, saliency mapping, and feature attribution analysis are incorporated to visualize the imaging regions influencing diagnostic decisions. In addition, synthetic image validation procedures are integrated to detect potential artifacts, unrealistic structures, or mode collapse effects that may negatively affect model reliability. The proposed framework provides a scalable and privacy-conscious solution for addressing one of the most critical barriers in rare disease artificial intelligence research—the lack of sufficient training data. By leveraging GAN-generated synthetic medical images, the framework enables the development of more accurate, robust, and clinically deployable diagnostic systems capable of supporting early detection and improved management of rare diseases across diverse healthcare environments.

 

Keywords

References

  • [1] El-Kenawy, E. S., & Eid, M. (2020). Hybrid gray wolf and particle swarm optimization for feature selection. Int. J. Innov. Compu t. Inf.
  • [2] El-Kenawy, E. S. M., Mirjalili, S., Ibrahim, A., Alrahmawy, M., El -Said, M., Zaki, R. M., & Eid, M. M. (2021). Advanced meta -
  • [3] Khodadadi, N., Abualigah, L., El -Kenawy, E. S. M., Snasel, V., & Mirjalili, S. (2022). An archive -based multi -objective arithmetic
  • [4] Ibrahim, A., Mirjalili, S., El -Said, M., Ghoneim, S. S., Al -Harthi, M. M., Ibrahim, T. F., & El -Kenawy, E. S. M. (2021). Wind speed
  • [5] Abdelhamid, A. A., El -Kenawy, E. S. M., Alotaibi, B., Amer, G. M., Abdelkader, M. Y., Ibrahim, A., & Eid, M. M. (2022). Robust
  • [6] El-Kenawy, E. S. M., Mirjalili, S., Alassery, F., Zhang, Y. D., Eid, M. M., El -Mashad, S. Y., ... & Abdelhamid, A. A. (2022). Novel
  • [7] Hassib, E. M., El -Desouky, A. I., Labib, L. M., & El -Kenawy, E. S. M. (2020). WOA+ BRNN: An imbalanced big data classification
  • [8] Eid, M. M., El -kenawy, E. S. M., & Ibrahim, A. (2021, March). A binary sine cosine -modified whale optimization algorithm for feature
  • [9] El-Sayed Towfek, M. (2018). El -kenawy. Trust Model for Dependable File Exchange in Cloud Computing. International Journal of
  • [10] Abdelhamid, A. A., Towfek, S. K., Khodadadi, N., Alhussan, A. A., Khafaga, D. S., Eid, M. M., & Ibrahim, A. (2023). Waterwhee l plant
  • [11] El-Kenawy, E. S. M., Abdelhamid, A. A., Ibrahim, A., Mirjalili, S., Khodadad, N., Alduailij, M. A., ... & Khafaga, D. S. (2023). Al-
  • [12] Alhussan, A. A., Abdelhamid, A. A., El -Kenawy, E. S. M., Ibrahim, A., Eid, M. M., Khafaga, D. S., & Ahmed, A. E. (2023). A binary
  • [13] Alhussan, A. A., Khafaga, D. S., Abotaleb, M., Mishra, P., & El -Kenawy, E. S. M. (2024). Global Potato Production Forecasting Based
  • [14] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
  • [15] Mishra, P., Alhussan, A. A., Khafaga, D. S., Lal, P., Ray, S., Abotaleb, M., ... & El -Kenawy, E. S. M. (2024). Forecasting production of
  • [16] El-Kenawy, E. S. M., Mirjalili, S., Abdelhamid, A. A., Ibrahim, A., Khodadadi, N., & Eid, M. M. (2022). Meta -heuristic optimization
  • [17] Abdelhamid, A. A., El -Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Khafaga, D. S., Alharbi, A. H., ... & Saber, M. (2022).
  • [18] Alharbi, A. H., Towfek, S. K., Abdelhamid, A. A., Ibrahim, A., Eid, M. M., & Khafaga, D. S. & Saber, M.(2023). Diagnosis of
  • [19] Alhussan, A. A., & Towfek, S. K. (2024). 5G Resource Allocation Using Feature Selection and Greylag Goose Optimization Algori thm.
  • [20] Towfek, S. K., & Alhussan, A. A. (2024). Potato Production Forecasting Based on Balance Dynamic Biruni Earth Radius Algorithm for
  • [21] Abdelhamid, A. A., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Eed, M. (2024). Potato harvesting pred iction
  • [22] Eed, M., Alhussan, A. A., Qenawy, A. S. T., Osman, A. M., Elshewey, A. M., & Arnous, R. (2024). Potato Consumption Forecastin g
  • [23] Mahmood, S., Sun, H., Iqbal, A., Alhussan, A. A., & El -kenawy, E. S. M. (2024). Green finance, sustainable infrastructure, and green
  • [24] El-Kenawy, E. S. M., Alhussan, A. A., Khodadadi, N., Mirjalili, S., & Eid, M. M. (2024). Predicting potato crop yield with machi ne
  • [25] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
  • [26] Ghasemi, M., Khodadadi, N., Trojovský, P., Li, L., Mansor, Z., Abualigah, L., ... & El -Kenawy, E. S. M. (2025). Kirchhoff’s law
  • [27] Yassen, M. A., El -Kenawy, E. S. M., Abdel -Fattah, M. G., Ismail, I., & Mostafa, H. E. D. S. (2025). Explainable artificial intelligence
  • [28] Radwan, M., Alhussan, A. A., Ibrahim, A., & Tawfeek, S. M. (2025). Potato leaf disease classification using optimized machine learning
  • [29] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
  • [30] El-kenawy, E. S. M., Alhussan, A. A., Mattar, E. A., & Radwan, M. (2026). Feature selection and hyperparameter tuning in transfo rmer -
  • [31] Chen, L., Xu, C., Lim, W. H., Sharma, A., Tiang, S. S., Chong, K. S., ... & Khafaga, D. S. (2025). Transparent and reliable c onstruction
  • [32] Khodadadi, N., Towfek , S. K., Zaki, A. M., Alharbi, A. H., Khodadadi, E., Khafaga, D. S., ... & Eid, M. M. (2025). Predicting
  • [33] Alhussan, A. A., El -Kenawy, E. S. M., Khafaga, D. S., Alharbi, A. H., & Eid, M. M. (2025). Groundwater resource prediction and
  • [34] Mozhdehi, A. T., Khodadadi, N., Aboutalebi, M., El -Kenawy, E. S. M., Hussien, A. G., Zhao, W., ... & Mirjalili, S. (2025). Divine
  • [35] Lopes, J. M., Pinho, C. S., Martins, R. C., & Bandeira, T. (2026). Sustainability Gets Smarter: Competitive Pressure and AI L eading the
  • [36] Radwan, M., Ibrahim, A., Abdelsalam, M. M., Alhussan, A. A., Mattar, E. A., & El -Kenawy, E. S. M. (2026). Optimizing solar and wind
  • [37] Alhussan, A. A., El -Kenawy, E. S. M., Eid, M. M., & Khodadadi, N. (2026). Hybrid Al -Biruni and Puma Optimization (BERPO) for
  • [38] 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
  • [39] El-Kenawy, E. S. M., Khodadadi, N., Mirjalili, S., Zaki, A. M., Ibrahim, A., Alhussan, A. A., ... & Eid, M. M. (2026). Glider sn ake
  • [40] Mekaret, F., Rabehi, A., Zebentout, B., Tizi, S., Douara, A., Bellucci, S., ... & Alhussan, A. A. (2024). A comparative study of Schottky
  • [41] KOUADRI, Ali, RABEHI, Abdelhalim, BENZIANE, Ali, et al. A Robust Multi -Transform Watermarking Scheme for Medical Images
  • [42] Tibermacine, I. E., Russo, S., Scarano, G., Tedesco, G., Rabehi, A., Alhussan, A. A., ... & Napoli, C. (2025). Conditional VA E for
  • [43] Kouadri, A., Benziane, A., Rabehi, A., Rabehi, A., Alhussan, A. A., Khafaga, D. S., & El -Kenawy, E. S. M. (2025). A novel hybrid
  • [44] Russo, S., Tibermacine, I. E., Randieri, C., Rabehi, A., Alharbi, A. H., El -Kenawy, E. S. M., & Napoli, C. (2025). Exploiting facial
  • [45] Bentegri, H., Rabehi, M., Kherfane, S., Nahool, T. A., Rabehi, A., Guermoui, M., ... & El -Kenawy, E. S. M. (2025). Assessment of
  • [46] Tibermacine, I. E., Russo, S., Citeroni, F., Mancini, G., Rabehi, A., Alharbi, A. H., ... & Napoli, C. (2025). Adversarial de noising of EEG
  • [47] Ouahabi, M. S., Benyounes, A., Barkat, S., Ihammouchen, S., Rekioua, T., Rabehi, A., ... & Alharbi, A. H. (2025). Real -time sensor fault
  • [48] Belaid, A., Guermoui, M., Khelifi, R., Arrif, T., Chekifi, T., Rabehi, A., ... & Alhussan, A. A. (2024). Assessing suitable a reas for PV
  • [49] Mehallou, A., M’hamdi, B., Amari, A., Teguar, M., Rabehi, A., Guermoui, M., ... & Khafaga, D. S. (2025). Optimal multiobjecti ve
  • [50] Rabehi, A., El -Hadi, M., Benmahmoud, S., Rabehi, A., Alharbi, A. H., & El -Kenawy, E. S. M. (2025). SOCA -CFAR Processor in A
  • [51] Bakria, D., Beladel, A., Korich, B., Teta, A., Mohammedi, R. D., Laouid, A. A., ... & El -kenawy, E. S. (2025). A novel enhanced Grey
Download