Health Problem Analysis from Nail Image using Deep Learning Technique

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Nattavadee Hongboonmee
Jutamas Kanyaprasit


The objectives of this research are (1) to develop a nail image classification model for health problem analysis using deep learning technique; (2) to develop a system for analyzing health problems from nails image; and (3) to assess the system performance. This research was conducted by 400 sample images of five nail types such as psoriasis nails image, rheumatoid nails image, anemia nails image, melanoma nail images and normal nail image. The sample images were trained and generated classifier models using deep convolutional neural network. The results show that there is 90.20% of accuracy for the sample image set. Then develop the user interface through the application on the android operating system. The application will run the model through a series of instructions to analyze health problems from nail images. The results of the study showed that the applications that developed able to analyze health problems from nail images, the performance is good. It has an average accuracy of 78.00%. The results of the application satisfaction assessment by the users were at a good level. The average is 4.09. Therefore, the presented methods and applications can used as a tool to help analyze health problems that may arise from nail images.

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How to Cite
N. Hongboonmee and J. Kanyaprasit, “Health Problem Analysis from Nail Image using Deep Learning Technique”, JIST, vol. 11, no. 2, pp. 10–20, Dec. 2021.
Research Article: Soft Computing (Detail in Scope of Journal)


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