Treffer: Detection and Diagnosis of Skin Diseases Using Residual Neural Networks (RESNET).

Skin diseases have become prevalent in the present times. It has been observed in a study that every year the percentage of global population suffering from skin diseases is 1.79%. These diseases have a potential to become extremely dangerous if they are not treated in the nascent stages. It is extremely important that skin diseases are detected and diagnosed at the starting stages so that serious risks to life are avoided. Often, exhaustive tests are required so as to arrive on a conclusion regarding skin condition, the patient may be affected with. Thus, an expert system is required that has the ability to identify diseases and propose the required diagnosis. Presently, only a few solutions are available for diagnosis of skin diseases using computerized system but this is an era which is under extensive research and can be developed further. As the existing system has certain loopholes, this system attempts to override the present problems by applying a different approach. As a result of comparison of results from numerous research papers, an expert system has been developed by choosing residual neural networks (ResNet) and this system can be used to aid skin specialists in identifying and diagnosing various major diseases of skin like (Eczema, Psoriasis & Lichen Planus, Benign Tumors, Fungal Infections and Viral Infections) in more effective and efficient manner. The causes for identified skin disease can be outlined through this system and treatment can be provided. We have used Python language for implementing the proposed expert system that uses a 50-layer ResNets for training a dataset that has been taken from DERMNET. We achieved an accuracy of 95% using ResNet for training of the model and prediction of results at an epoch value of 10. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Image & Graphics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)