KEYWORDS: Eye models, Education and training, Data modeling, Machine learning, Eye, RGB color model, Information technology, Information science, Diagnostics, Color
Anemia is one of the most common social problem, so much so that one in ten people is said to be anemic. Anemia can be diagnosed by observing the coloration of the eyelid conjunctiva or by drawing blood. However, both cases require specialized knowledge and are not easy methods in terms of time and cost. This paper proposes a new method for estimating anemia from facial images. First, the eyelid conjunctiva region is extracted using a cascade classifier. Next, the obtained images were input into a model using a convolutional neural network (CNN), which had already learned the characteristics of anemia, to create a system that automatically estimates the state of anemia. Compared to a model using the standard multilayer perceptron (MLP), the standard MLP-based model had anemia estimation accuracy of 66.7%, while the CNN-based model had an accuracy of 87.5%. The results confirmed the effectiveness of the proposed method.
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