Hybrid Approach for Skin Disease Classification: Integrating Machine learning and Deep Learning

Abstract

Abstract: Skin infections are more common than many other diseases, and they can be caused by various factors such as infectious pollution, microorganisms, awareness, and infections. With the advancement of lasers and photonics-based medical technology, the diagnosis of skin infections has become faster and more accurate. However, the cost of such diagnosis is still limited and expensive. Therefore, image processing techniques are used to develop an automated assessment system for dermatology at an early stage. The extraction of features plays a crucial role in accurately and quickly diagnosing skin diseases. PC vision plays an important role in the detection of skin diseases in various ways. This study focuses on four skin diseases: ringworm, nail parasite, psoriasis, and atopic dermatitis. Convolutional neural networks have achieved close to or even better performance than humans in the imaging field. The skin diseases are classified using a machine learning algorithm, i.e., random forest, which achieves an accuracy of 98.23% after 100 epochs.