This manuscript presents an approach to classify tongue abnormality related to Diabetes Mellitus (DM) following Western Medicine (WM) approach. Glossitis abnormality is one of the common tongue abnormalities that affects patients who suffer from Diabetes Mellitus (DM). The novelty of the proposed approach is attributed to utilising visual signs that appear on tongue due to Glossitis abnormality causes by high blood sugar level in the human body. The test for the blood sugar level is inconvenient for some patients in rural and poor areas where medical services are minimal or may not be available at all. To screen and monitor human organ effectively, the proposed computer aided model predicts and classifies abnormality appears on the tongue or tongue surface using visual signs caused by the abnormality. The visual signs were extracted following a logically formed medical approach, which complies with Western Medicine (WM) approach. Using Random Forest classifier on the extracted visual tongue signs, from 572 tongue samples for 166 patients, the experimental results have shown promising accuracy of 95.8% for Glossitis abnormality.
|Title of host publication||Lecture Notes in Engineering and Computer Science|
|Publication status||Published - 7 Jul 2017|
|Event||World Congress on Engineering 2017 - Imperial College London, London, United Kingdom|
Duration: 5 Jul 2017 → 7 Jul 2017
|Conference||World Congress on Engineering 2017|
|Period||5/07/17 → 7/07/17|
- Centre for Intelligent Visual Computing Research