Dermatological Infrared Thermal Imaging with Human-Machine Interaction Image Diagnostics Interface Using DenseNet

Objective: and Background: The integration of infrared thermal imaging with advanced neural network models to revolutionize dermatological diagnostics is the objective of this study. By leveraging the sophisticated features of DenseNet and the additional insights provided by infrared thermal imaging, the accuracy and categorization of skin conditions diagnostic is enhanced. Methodology: In pursuit of groundbreaking dermatological diagnostics, we leverage a dataset comprising 1008 meticulously preprocessed high-resolution skin images, curated by specialists. Optimal model performance is achieved through transfer learning on the DenseNet architecture, renowned for efficiently capturing intricate skin features. The integration of infrared thermal imaging augments this model, providing additional physiological insights into skin conditions. Classification hinges on a fully connected softmax layer, and a classbalanced loss approach addresses data imbalance challenges. The utilization of Monte Carlo Cross-Validation further enhances device capabilities, resulting in a remarkable overall recognition rate of 96.35%. Results: The DenseNet model, which processes infrared thermal data, consistently achieves an impressive 96.35% overall recognition rate, with a sensitivity of 83.25%. Comparative analysis showcases its superiority, delivering exceptional accuracy in skin condition identification. This multidimensional approach, combining visual and thermal information, propels the precision of dermatological diagnostics to unprecedented heights. Conclusion: This study introduces a transformative methodology in dermatological diagnostics, where the integration of infrared thermal imaging and DenseNet significantly enhances accuracy and categorization capabilities. The results underscore the potential of this multidimensional approach to redefine the standards of skin condition identification, marking a significant advancement in the field of dermatology. The confluence of technology and healthcare promises to usher in new frontiers of precision and accessibility, shaping the future of medical imaging and diagnostics.