ExpACVO-Hybrid Deep learning: Exponential Anti Corona Virus Optimization enabled Hybrid Deep learning for tongue image segmentation towards diabetes mellitus detection

Mathew, Jimsha K; Sathyalakshmi, S.

doi:10.1016/j.bspc.2023.104635

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Deepa and Banerjee 79 also applied the SVM method as a classifier and used particle swarm optimization (PSO) technique to tune the parameters and enhance its performance; this time, deep features of the tongue (such as color, texture, coating, tooth-marked, and red spots) were utilized from CNN DenseNet framework. Mathew and Sathyalakshmi 80 also developed an optimization-driven hybrid deep learning method for diabetes detection based on tongue images. A proposed ExpACVO optimization algorithm was used for Deep Q-Network classifier training.…”

Section: Tongue Image For Disease Diagnosismentioning

confidence: 99%

A survey of artificial intelligence in tongue image for disease diagnosis and syndrome differentiation

Liu

Peng

et al. 2023

DIGITAL HEALTH

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The rapid development of artificial intelligence technology has gradually extended from the general field to all walks of life, and intelligent tongue diagnosis is the product of a miraculous connection between this new discipline and traditional disciplines. We reviewed the deep learning methods and machine learning applied in tongue image analysis that have been studied in the last 5 years, focusing on tongue image calibration, detection, segmentation, and classification of diseases, syndromes, and symptoms/signs. Introducing technical evolutions or emerging technologies were applied in tongue image analysis; as we have noticed, attention mechanism, multiscale features, and prior knowledge were successfully applied in it, and we emphasized the value of combining deep learning with traditional methods. We also pointed out two major problems concerned with data set construction and the low reliability of performance evaluation that exist in this field based on the basic essence of tongue diagnosis in traditional Chinese medicine. Finally, a perspective on the future of intelligent tongue diagnosis was presented; we believe that the self-supervised method, multimodal information fusion, and the study of tongue pathology will have great research significance.

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Section: Tongue Image For Disease Diagnosismentioning

confidence: 99%