Automated Skin Disease Detection and Classification Using Advanced Deep Learning Techniques

Abstract

Diseases of the skin are rampant. It is one major health problem that affects millions across the globe yearly, with much physical and emotional long-lasting debilitation. Routine diagnosis tends to deal with only one type and particular condition of skin disease at any given time; thus, in some cases, ruining the overall patient management and reaching wrong diagnoses. Introduction of the newest, intelligent software system: a computer-aided diagnosis based on advanced deep-learning approaches, such as Convolutional Neural Networks and data mining algorithms, is capable of performing multiple skin disease diagnoses at once. By applying CNN, the system automates the dermatology image data feature extraction and improves the diagnosis of such diseases. By allowing mining algorithms, the system also allows continuous extraction and analysis on patient data with real-time prediction on skin conditions. Integration of all of these is expected to improve diagnosis accuracy and efficiency and serves as an asset for healthcare professionals in managing skin diseases. The final summons are aimed toward improving patient care operations while enabling better diagnostics in dermatology.