Expert Automated System for Prediction of Multi-Type Dermatology Sicknesses Using Deep Neural Network Feature Extraction Approach - Kalaivani, A.; Karpagavalli, S.; Gulati, Kamal (International Journal of Intelligent Systems and Applications in Engineering, 2023)
One of the most prevalent illnesses on the planet is skin issues. Due to the complexity of types of skin, and hair types, it is difficult to evaluate it despite its popularity. Consequently, skin conditions pose a serious public health danger. When they reach the invasive stage of evolution, they become harmful. Medical professionals are very concerned about dermatological disorders. The number of people who suffer from skin illnesses is growing substantially as a result of rising pollution and bad food. People frequently ignore the early indications of skin conditions. A hybrid approach can minimize human judgment, producing positive results quickly. A thorough examination suggests that frameworks for recognizing various skin disorders may be built using deep learning techniques. To find skin illnesses, it is necessary to distinguish between the skin and non-skin tissue. Through the use of feature extraction-based deep neural network approaches, a classification system for skin diseases was established in this study. The main goal of this system is to anticipate skin diseases accurately while also storing all relevant state data efficiently and effectively for precise forecasts. The significant issues have been addressed, and a unique, feature extraction-based deep learning model is introduced to assist medical professionals in properly detecting the type of skin condition. The pre-processing stage is when the input dataset is first supplied, helping to clear the image of any undesired elements. Then, for the training phase, the proposed Feature Extraction Based Deep Neural Network (FEB-DNN) is fed the features collected from each of the pre-processed frames. With the use of measured parameters, the classification system categorizes incoming treatment data as various skin conditions. Finding the ideal weight values to minimize training error is crucial while learning the proposed framework. In this study, an optimization strategy is used to optimize the weight in the structure. Based on the feature extraction approach, the suggested multi-type framework for diagnosing skin diseases has a 91.88% of accuracy rate for the HAM image dataset and identifies several skin disorder subtypes than the earlier models that can aid in treatment response and decision-making which also help doctors make an informed decision.