Tongue Disease Prediction Based on Machine Learning Algorithms Article Swipe
Ali Raad Hassoon
,
Ali Al‐Naji
,
Ghaidaa A. Khalid
,
Javaan Chahl
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/technologies12070097
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/technologies12070097
The diagnosis of tongue disease is based on the observation of various tongue characteristics, including color, shape, texture, and moisture, which indicate the patient’s health status. Tongue color is one such characteristic that plays a vital function in identifying diseases and the levels of progression of the ailment. With the development of computer vision systems, especially in the field of artificial intelligence, there has been important progress in acquiring, processing, and classifying tongue images. This study proposes a new imaging system to analyze and extract tongue color features at different color saturations and under different light conditions from five color space models (RGB, YcbCr, HSV, LAB, and YIQ). The proposed imaging system trained 5260 images classified with seven classes (red, yellow, green, blue, gray, white, and pink) using six machine learning algorithms, namely, the naïve Bayes (NB), support vector machine (SVM), k-nearest neighbors (KNN), decision trees (DTs), random forest (RF), and Extreme Gradient Boost (XGBoost) methods, to predict tongue color under any lighting conditions. The obtained results from the machine learning algorithms illustrated that XGBoost had the highest accuracy at 98.71%, while the NB algorithm had the lowest accuracy, with 91.43%. Based on these obtained results, the XGBoost algorithm was chosen as the classifier of the proposed imaging system and linked with a graphical user interface to predict tongue color and its related diseases in real time. Thus, this proposed imaging system opens the door for expanded tongue diagnosis within future point-of-care health systems.
Related Topics
Concepts
Artificial intelligence
YCbCr
Tongue
Naive Bayes classifier
Color space
HSL and HSV
Support vector machine
Computer science
RGB color model
Random forest
Algorithm
Classifier (UML)
Machine learning
Computer vision
Pattern recognition (psychology)
Image processing
Color image
Image (mathematics)
Medicine
Pathology
Virology
Virus
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/technologies12070097
- https://www.mdpi.com/2227-7080/12/7/97/pdf?version=1721118951
- OA Status
- gold
- Cited By
- 10
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400290745
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400290745Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/technologies12070097Digital Object Identifier
- Title
-
Tongue Disease Prediction Based on Machine Learning AlgorithmsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-28Full publication date if available
- Authors
-
Ali Raad Hassoon, Ali Al‐Naji, Ghaidaa A. Khalid, Javaan ChahlList of authors in order
- Landing page
-
https://doi.org/10.3390/technologies12070097Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-7080/12/7/97/pdf?version=1721118951Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2227-7080/12/7/97/pdf?version=1721118951Direct OA link when available
- Concepts
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Artificial intelligence, YCbCr, Tongue, Naive Bayes classifier, Color space, HSL and HSV, Support vector machine, Computer science, RGB color model, Random forest, Algorithm, Classifier (UML), Machine learning, Computer vision, Pattern recognition (psychology), Image processing, Color image, Image (mathematics), Medicine, Pathology, Virology, VirusTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 8, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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