Hybrid GAN-LSTM framework for diabetic foot ulcer image synthesis and automated diagnosis

The application of artificial intelligence (AI) in the analysis of medical images faces significant challenges, chiefly due to the scarcity of well-labeled datasets that are crucial for training sophisticated diagnostic models. To address this issue, we developed three hybrid models that integrate generative components with classification systems. These models differ in their classification architectures to compare the effectiveness of generative data augmentation across various diagnostic applications. By generating high-quality synthetic images of Diabetic Foot Ulcers (DFUs) using advanced network techniques, we ensure both realistic image quality and robust clinical relevance, while abstracting low-level implementation details to focus on the stability and fidelity of the generative process.

In our methodology, we introduce temporal dependency modeling within the latent feature space, despite the non-temporal nature of DFU images. The latent representations are systematically organized into ordered sequences, enabling Long Short-Term Memory (LSTM) layers to identify structured spatial relationships among varying wound regions. This sequential processing captures long-range spatial dependencies, thereby modeling consistencies between distant lesion areas and promoting anatomical coherence-challenges that conventional convolutional operations struggle to address. The three hybrid models incorporated in this study feature distinct generator backbones:1. Baseline CNN-LSTM Architecture - Focused on efficient spatial modelling.2. EfficientNetV2M-LSTM Model - Emphasizing high-capacity feature extraction.3. EfficientNetV2S-LSTM Model - Striking a balance between computational efficiency and synthesis quality.Additionally, we employed WGAN-GP + LSTM in one of our models to enhance stable generative training and spatial consistency. This approach utilizes a critic network instead of a traditional discriminator, assessing the discrepancies between real and synthetic datasets to promote stable image generation and mitigate mode collapse. The generative models were trained on a carefully curated dataset comprising 5,894 clinically annotated DFU images from Lancashire Teaching Hospital, representing a variety of ulcer types and severities. Annotations were conducted by three seasoned healthcare professionals specializing in diabetic foot care.

Our findings demonstrate that the implementation of synthetic images significantly enhances disease classification accuracy and boosts the effectiveness of automated diagnostic systems for DFUs. By maintaining clinically relevant variability in ulcer appearances, the generated images contribute to the development of robust models capable of performing effectively under real-world conditions, which is critical for deployment in screening, triage, and remote wound assessment workflows.

The advancements realized through the integration of generative models in medical image analysis pave the way for real-time clinical applications such as early screening, patient prioritization during triage, and telemedicine assessments of wounds. This is especially crucial for healthcare systems in underserved or remote areas. The ability to leverage synthetic data not only supports improved diagnostic capabilities but also ensures that models remain adaptable to the variability present in clinical scenarios, ultimately enhancing patient care and resource allocation in diabetic foot ulcer management.