SENSOR-INDEPENDENT FRAMEWORK FOR TONGUE COLOR CLASSIFICATION SYSTEM

Chebastian, Dhiaz (2021) SENSOR-INDEPENDENT FRAMEWORK FOR TONGUE COLOR CLASSIFICATION SYSTEM. S1 thesis, Universitas Atma Jaya Yogyakarta.

Preview	Text (Dhiaz Chebastian) 170709205_chap0.pdf Download (130kB) | Preview
Preview	Text 170709205_chap1.pdf Download (51kB) | Preview
	Text 170709205_chap2.pdf Restricted to Registered users only Download (58kB)
	Text 170709205_chap3.pdf Restricted to Registered users only Download (1MB)
	Text 170709205_chap4.pdf Restricted to Registered users only Download (1MB)
	Text 170709205_chap5.pdf Restricted to Registered users only Download (1MB)
Preview	Text 170709205_chap6.pdf Download (163kB) | Preview

Abstract

Tongue diagnosis is one of noninvasive methods to diagnose the condition of a patient’s internal organs in Traditional Chinese Medicine (TCM). Since this way is noninvasive, it encourages self-diagnosis at home. One of the essential aspects of this diagnosis is the tongue color which is prone to being influenced by the lighting environment and different sensor sensitivity. It brings ambiguity and problems to get a consistent diagnosis result in self-diagnosis. In facing this problem, many researchers have found great solutions to make a reliable automated tongue diagnosis (ATD) system which potentially can solve the problem for a smartphone. However, the system can still be improved to minimize the error which is caused by the different sensor sensitivity and unoptimized parameters. For improving it, this paper suggests an alternative framework for tongue color classification system (TCCS) as the part of ATD system in getting more consistent color before going to the disease prediction in ATD system. The improvisation is done by implementing the kernel partial least square regression (K-PLSR) optimized algorithm in color correction of framework and other reliable algorithms. After doing an experiment by implementing this framework in several smartphones, this framework can show an improvement and can be used in more than one sensor and environment. As a result, it gets a more consistent and objective tongue color classification result.

Actions (login required)

View Item