Skin Disease Detection using Machine Learning Techniques

Skin diseases are growing rapidly in the worldwide, and their proper diagnosis with accuracy is needed for effective treatment. As seen in the previous years the results of treatments related to skin diseases are not up to the mark as needed due to various reasons like manual examinations of skin lesions, techniques used are more time consuming and these treatments are hindered due to human errors also. After seeing these complications, new technologies are developed as boon to skin treatments. These machine learning techniques consists of preprocessing operations like removing noise, standardizing the input, improving quality of image data for better classification. The hybrid approach has many benefits in the development of classical image analysis and deep learning to enhance classification, accuracy, robustness and interpretability. The results on various datasets available publicly like as HAM10000 and ISIC illustrate that the model designed has a higher diagnostic performance as compared to previous traditional methods, this automated classification of skin diseases has more potential as per analysis.