<p>Diabetic Foot Ulcers (DFUs) are a severe difficulties of diabetes, often resulting from peripheral vascular disease, neuropathy, and poor glycemic control. If left undetected, DFUs can lead to severe infections, amputations, and increased mortality rates. Despite advancements in deep learning for medical image analysis, existing DFU detection methods suffer from low accuracy, high false-positive rates, and limited generalizability, making them unsuitable for real-world clinical deployment. To point out this challenge, this study proposes a novel DFU detection framework Cell Attention Networks with Genghis Khan Shark Optimization (CANet-GKSOpt) which integrates multiple cutting-edge AI techniques to enhance diagnostic precision. The proposed method utilizes Block-Batching Domain-Guided Filtering (2B-DGF) for noise reduction and image enhancement, followed by feature extraction using Hamiltonian Quantum Generative Adversarial Networks (HQGANet) to capture complex ulcer patterns. Cell Attention Networks (CANet) are then employed for refined classification by focusing on critical image regions. To further optimize accuracy and computational efficiency, the Genghis Khan Shark Optimization (GKSO) algorithm is applied for parameter tuning. The model is evaluated on the DFU2020 dataset and achieves a satisfying accuracy around 99% and 99.8% of sensitivity, significantly outperforming existing state-of-the-art methods. These results demonstrate the robustness of CANet-GKSOpt in accurately detecting DFUs, reducing false positives, and improving classification efficiency. The proposed approach contributes to the evolution of a highly effective AI-assisted diagnostic tool, which, with further validation on multi-center datasets and real-world clinical settings, has the potential to revolutionize DFU detection and management.</p>