Detection of Kidney Organ Condition Using Hidden Markov Models - Siska Anraeni; Ingrid Nurtanio; Indrabayu Indrabayu (Indonesian Journal of Electrical Engineering and Computer Science, 2015)
The frequencies of chronic kidney disease are likely to continue to increase worldwide. So people need to take a precaution, which is by maintaining kidney health and early detection of renal impairment by analyzing the composition of the iris is known as iridology. This paper presents a novel approach using a one-dimensional discrete Hidden Markov Model (HMM) classifier and coefficients Singular Value Decomposition (SVD) as a feature for image recognition iris to indicate normal or abnormal kidney. The system has been examined on 200 iris images. The total images of the abnormal kidney condition were 100 images and those for the normal kidney condition were 100 images. The system showed a classification rate up to 100% using total of image for training and testing the system unspecified, resize iris image 56x46 pixels, coefficient values U(1,1), Σ(1,1) and Σ(2,2), quantized values [18 10 7], and classify by 7-state HMM with .pgm format database.