Abstract
This study aims to obtain accuracy for classification of diabetes mellitus based on fingernail images using neural network. The data is fingernail images with fasting conditions and non-fasting conditions. In each condition, data is divided into three namely diabetes, prediabetes and normal data. Images in each data are 100 images. Methods were preprocessing, GLCM, neural network (NN) and accuracy. Preprocessing were cropping and grayscaling. GLCM were contrast, entropy, homogeneity, energy and correlation features. In NN, number of epochs used is 1000. Images in the training data is 240. The accuracy were 100% for fasting condition and 85% for non-fasting condition.
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