Prototyping Digital Tongue Diagnosis System on Raspberry Pi


  • Muhammad Azrae Yusof National Defense University of Malaysia
  • Nur Diyana Kamarudin National Defense University of Malaysia
  • Syarifah Bahiyah Rahayu National Defense University of Malaysia
  • Siti Noormiza Makhtar National Defense University of Malaysia
  • Hassan Mohamed National Defense University of Malaysia
  • Noorain Tajudin Sophia Tech Campus


Tongue diagnosis prototype, tongue segmentation, tongue classification, k-means clustering, Support Vector Machine.


Tongue inspection is a complementary diagnosis method that widely used in Traditional Chinese Medicine (TCM) by inspecting the tongue body constitution to decide the physiological and pathological functions of the human body. Since tongue manifestation is done by practitioner’s observation using naked eye, many limitations can affect the diagnosis result including environment condition and experiences of the practitioner. Lately, tongue diagnosis has been widely studied in order to solve these limitations via digital system. However, most of recent digital system are bulky and not equipped with intelligent diagnosis system that can finally predict the health status of the patient. In this research, digital tongue diagnosis system that uses intelligent diagnosis consisted of image segmentation analysis, tongue coating recognition analysis, and tongue color classification has been embedded on Raspberry Pi. Tongue segmentation implements Hue, Saturation and Value (HSV) color space with Brightness Conformable Multiplier (BCM) for adaptive brightness filtering to recognized tongue body accurately while eliminating perioral area.  Tongue Coating Recognition uses threshold method to detect tongue coating and eliminate the unwanted features including shadow. Tongue color classification uses hybrid method consisted of k-means clustering and Support Vector Machine (SVM) to classify between red, light red and deep red tongue and further gave diagnosis based on color. This experiment concluded that it is feasible to embed the algorithm on Raspberry Pi to promote system portability while attaining similar accuracy for future telemedicine.


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How to Cite

Yusof, M. A., Kamarudin, N. D. ., Rahayu, S. B. ., Makhtar, S. N. ., Mohamed, H., & Tajudin, N. (2021). Prototyping Digital Tongue Diagnosis System on Raspberry Pi . International Journal of Integrated Engineering, 13(5), 146–155. Retrieved from