Article,
Nail Disease Detection and Classification Using Deep Learning
CENTRAL ASIAN JOURNAL OF MEDICAL AND NATURAL SCIENCES, (2022)
Abstract
Many disorders are identified in the early stages of diagnosis by analyzing the human hand’s nails. The colour of a person’s nails can aid in diagnosing certain medical conditions. The suggested approach, in this situation, leads to illness diagnosis decision-making. Human nail art is used to feed the system. The technology analyses nail photos and extracts disease-specific nail characteristics. The human nail has numerous characteristics, and the suggested system detects illness by changing the colour of the nail. The initial training set data is extracted from an image of a patient’s nails with a certain condition and processed with the Weka tool. Nail To obtain the desired results, the image’s feature results are compared to the training dataset. Deformation of the nail unit is referred to as nail disease. Nail units have their sickness class because of their distinct indications, symptoms, causes, and consequences that may or may not be related to other medical illnesses. Nail problems are still unknown and difficult to diagnose. This study proposes a fresh deep learning system for identifying and categorizing nail disorders from photos. CNN models (CNN) are combined in this framework to extract features. This research was also contrasted with certain other province algorithms (Support vector, ANN, K - nearest neighbors, and RF) evaluated on datasets and showed positive results.
