Journal of Artificial Intelligence and Capsule Networks
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How to Cite
L., Suriya, Suganthi R., Meraklin B., and Muthu Kumari C. 2026. “Non-Invasive Disease Prediction Using Tongue Images”. Journal of Artificial Intelligence and Capsule Networks 8 (3): 180-92. https://doi.org/10.36548/jaicn.2026.3.002.
Keywords
Tongue Image Analysis
YOLOv8
Convolutional Neural Network (CNN)
Disease Prediction
Medical Image Processing
Deep Learning
Non-Invasive Diagnosis
Artificial Intelligence
Abstract
Disease diagnosis at an early stage is extremely important for enhancing the treatment efficacy and decreasing costs of medical treatment. The tongue appearance is one of the non-invasive ways of detecting diseases as its appearance reflects some health disorders. This study proposes a deep learning framework for non-invasive disease prediction based on tongue images. The system uses the YOLOv8 model for detecting tongue regions, as well as the CNN model for feature extraction and disease classification. Tongue images from TMC-Tongue dataset are preprocessed by performing image resizing, normalization, and noise filtering to improve their quality and model performance. YOLOv8 detects the tongue region, whereas CNN extracts features related to different tongue diseases. The developed framework provides users with an application allowing uploading images, analyzing them in real time, and generating reports. Experimental evaluation achieved a classification accuracy of 84.2%, demonstrating the effectiveness of the proposed framework.References
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