Osteoarthritis Disease Detection System using Self Organizing Maps Method based on Ossa Manus X-Ray

Osteoarthritis is a disease found in the world, including in Indonesia. The purpose of this study was to detect the disease Osteoarthritis using Self Organizing mapping (SOM), and to know the procedure of artificial intelligence on the methods of Self Organizing Mapping (SOM). In this system, there are several stages to preserve to detect disease Osteoarthritis using Self Organizing maps is the result of photographic images rontgen Ossa Manus normal and sick with the resolution (150 x 200 pixels) do the repair phase contrast, the Gray scale, thresholding process, Histogram of process , and do the last process, where the process of doing training (Training) and testing on images that have kept the shape data (.text). the conclusion is the result of testing by using a data image, where 42 of data have 12 Normal image data and image data 30 sick. On the results of the process of training data there are 8 X-ray image revealed normal right and 19 data x-ray image of pain expressed is correct. Then the accuracy on the process of training was 96.42%, and in the process of testing normal true image 4 obtained revealed Normal, 9 data pain stated true pain and 1 data imagery hurts stated incorrectly, then the accuracy gained from the results of testing are 92,8%.

An Application of Backpropagation Artificial Neural Network Method for Measuring The Severity of Osteoarthritis

The examination of Osteoarthritis disease through X-ray by rheumatology can be classified into four grade of severity. This paper discusses about the application of artificial neural network backpropagation method for measuring the severity of the disease, where the observed X-ray range from wrist to fingers. The main procedures of system in this paper is divided into three, which are image processing, feature extraction, and artificial neural network process. First, an X-ray image digital (200x150 pixels and greyscale) will be thresholded, then extracted features based on probabilistic values of the color intensity of seven bit quantization result, and statistical textures. That feature values then will be normalizing to interval [0.1, 0.9], and then the result would be processing on backpropagation artificial neural network system as input to determine the severity of disease from an X-ray had input before it. From testing with learning rate 0.3, momentum 0.4, hidden units five pieces and about 132 feature vectors, this system had had a level of accuracy of 100% for learning data, 80% for learning and non-learning data, and 66.6% for non-learning data