TY - JOUR
T1 - Osteoarthritis Severity Determination using Self Organizing Map Based Gabor Kernel
AU - Anifah, L.
AU - Purnomo, M. H.
AU - Mengko, T. L.R.
AU - Purnama, I. K.E.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2018/2/22
Y1 - 2018/2/22
N2 - The number of osteoarthritis patients in Indonesia is enormous, so early action is needed in order for this disease to be handled. The aim of this paper to determine osteoarthritis severity based on x-ray image template based on gabor kernel. This research is divided into 3 stages, the first step is image processing that is using gabor kernel. The second stage is the learning stage, and the third stage is the testing phase. The image processing stage is by normalizing the image dimension to be template to 50 □ 200 image. Learning stage is done with parameters initial learning rate of 0.5 and the total number of iterations of 1000. The testing stage is performed using the weights generated at the learning stage. The testing phase has been done and the results were obtained. The result shows KL-Grade 0 has an accuracy of 36.21%, accuracy for KL-Grade 2 is 40,52%, while accuracy for KL-Grade 2 and KL-Grade 3 are 15,52%, and 25,86%. The implication of this research is expected that this research as decision support system for medical practitioners in determining KL-Grade on X-ray images of knee osteoarthritis.
AB - The number of osteoarthritis patients in Indonesia is enormous, so early action is needed in order for this disease to be handled. The aim of this paper to determine osteoarthritis severity based on x-ray image template based on gabor kernel. This research is divided into 3 stages, the first step is image processing that is using gabor kernel. The second stage is the learning stage, and the third stage is the testing phase. The image processing stage is by normalizing the image dimension to be template to 50 □ 200 image. Learning stage is done with parameters initial learning rate of 0.5 and the total number of iterations of 1000. The testing stage is performed using the weights generated at the learning stage. The testing phase has been done and the results were obtained. The result shows KL-Grade 0 has an accuracy of 36.21%, accuracy for KL-Grade 2 is 40,52%, while accuracy for KL-Grade 2 and KL-Grade 3 are 15,52%, and 25,86%. The implication of this research is expected that this research as decision support system for medical practitioners in determining KL-Grade on X-ray images of knee osteoarthritis.
UR - http://www.scopus.com/inward/record.url?scp=85043338628&partnerID=8YFLogxK
U2 - 10.1088/1757-899X/306/1/012071
DO - 10.1088/1757-899X/306/1/012071
M3 - Conference article
AN - SCOPUS:85043338628
SN - 1757-8981
VL - 306
JO - IOP Conference Series: Materials Science and Engineering
JF - IOP Conference Series: Materials Science and Engineering
IS - 1
M1 - 012071
T2 - 2nd International Conference on Innovation in Engineering and Vocational Education, ICIEVE 2017
Y2 - 25 October 2017 through 26 October 2017
ER -