Hybrid Optimization for Texture-Based Nail Disease Prediction Using Deep Learning

Abstract

A wide range of possible disorders may be characterized by color changes in nails; changes in shape of nails; or some type of texture irregularity in the nails that is indicative of a nail disorder. Each of these types of disorders could be the result of a number of different factors including a pathologic process, a previously unknown medical condition; stress; or some form of hereditary component. Of all the disorders of the nails that can occur, some of the most common include mycosis; onycholysis; psoriasis; and paronychia. While there are many similarities among the disorders of the nails, they also present some very significant differences in terms of the symptoms and the difficulty in treating them. An essential step in providing effective treatment for a patient with an underlying condition while protecting the health of their nail, includes an appropriate and thorough evaluation and monitoring of the patient's condition. The purpose of this study was to provide a comprehensive review of the study of the diseases of the nails and their diagnosis utilizing advanced optimization techniques and further to document the results of the experimental investigation. We utilized two metaheuristic optimization techniques called the Moth Fly Optimization (MFO) and Fruit Fly Optimization (FFO), in order to optimize the classification models used within this study. Our data set contained a maximum of 560 images used for training; 70 images used for validating the models; and 70 images of text, while the seven different forms of nail disease were illustrated. We have used variety of deep learning architecture and there accuracy such as 90% with EfficientNetB3, 86% with DenseNet121, 87.14% with ResNet50, 80% with InceptionV3, 90% with Xception, and 91% with MobileNetV2. We observed the highest level of precision of 1.0 with EfficientNetB3 and 0.95 with F1-Score. We were able to demonstrate both a global search capability and improved convergence speed through the use of our Adaptive Fruit Fly Optimization Algorithm (AFFOSM), resulting in an overall system accuracy of 91% using MobileNetV2, significantly better than other conventional methods. Our experimental results indicate that optimizing classification models for detecting nail diseases utilizing metaheuristic algorithms can greatly enhance reliability and investigative accuracy.