Hybrid Neural Tangent Kernel–SGD Optimization for Robust and Scalable Deep Learning Across Medical, Sensor, and Image Domains

Efficient and reliable training of deep neural networks remains a major challenge, particularly in fields such as medical imaging, sensor analysis, and ecological monitoring where data are often noisy, high-dimensional, and heterogeneous. Here we introduce a hybrid optimization framework that integrates Neural Tangent Kernel (NTK) theory with Stochastic Gradient Descent (SGD). The approach leverages NTK-based initialization to stabilize early training, followed by SGD fine-tuning to enable adaptability and scalability. I evaluate this method on diverse datasets including ISIC 2018 (skin lesion analysis), NIH ChestX-ray14 (radiography), Tiny ImageNet (natural image classification), and UCI HAR (sensorbased activity recognition). Across all benchmarks, the hybrid NTK-SGD method consistently outperforms NTK-only baselines and matches or exceeds standard SGD, while delivering faster convergence and improved robustness to adversarial noise. By uniting theory-driven stability with data-driven flexibility, NTKSGD offers a generalizable, interpretable, and computationally efficient training strategy. These results highlight its potential for cross-domain deployment in medical, environmental, and industrial AI applications, where both accuracy and resilience are critical.