Thermal radiation of tongue surface as a human computer interaction diagnostics technique based on image classification with software interface

Abstract

Backgroundand Purpose: Thermal radiation plays a pivotal role in addressing recognition limitations encountered by traditional Chinese medicine's tongue diagnosis. Modern thermal radiation diagnostic instruments offer enhanced objectivity, and in this study, we strive to further improve the diagnostic process through the integration of Human-Computer Interaction (HCI) principles. Our objective is to introduce the thermal radiation DenseNet ensemble model, synergizing the strengths of Convolutional Neural Networks (CNNs) and HCI principles to advance tongue image recognition in healthcare. This approach aims to enhance classification accuracy, even when faced with limited training data. MethodologyTransfer learning, utilizing fine-tuned CNNs and DenseNet, is employed for precise classification, with a specific focus on distinguishing tooth-marked tongues. A portable hand-held thermal radiation diagnostic instrument, seamlessly integrated with HCI, is developed for both clinical use and research. Experimental resultsMeticulously curated datasets, formed through the annotation of thermal radiation images, confirm the superior performance of the DenseNet architecture in tooth mark and tongue feature recognition. The proposed tooth mark tongue recognition model exhibits a significant accuracy improvement of approximately 25% compared to similar tasks in existing studies. Additionally, an AI health detector tailored for Traditional Chinese Medicine (TCM) thermal radiation image recognition is introduced, showcasing the seamless integration of HCI principles into healthcare applications. This innovative tool not only assesses users' health but also provides personalized health recommendations, illustrating the transformative potential of combining technology and healthcare. Discussion and conclusionIn addressing the limitations of traditional tongue diagnosis, thermal radiation emerges as a key factor enhancing objectivity and accuracy. The thermal radiation DenseNet ensemble model, along with HCI integration through the portable hand-held device, proves effective for improved TCM thermal radiation image identification in healthcare. The fusion of technology and HCI principles underscores the potential to elevate health assessments and offer valuable guidance to users.