Research on the Application of Deep Learning in Image Diagnosis in the Field of Healthcare

Deep learning has emerged as a transformative technology in medical image diagnosis, demonstrating significant potential for improving diagnostic accuracy. This study investigates the application of deep learning models in medical imaging analysis, focusing on pulmonary nodule detection in CT scans and skin lesion classification in dermatology. This study aims to explore how CNN-based deep learning models can be effectively applied to medical image diagnosis to enhance diagnostic accuracy and efficiency, investigate practical solutions to challenges such as data privacy and model interpretability, and analyze the clinical value and application potential of deep learning in medical image diagnosis. Key innovations included multi-scale feature fusion architectures and adaptive learning rate strategies. Despite promising results, challenges persist including data privacy constraints (addressed through federated learning frameworks), limited rare-class samples, and model interpretability gaps (partially resolved via gradient-weighted class activation mapping). This research confirms deep learning's clinical viability while highlighting critical considerations for real-world implementation.