Supervised classification of Dermatological diseases by Deep learning
It addresses the shortage of dermatological expertise, particularly in East Asian countries, by offering a machine learning solution to augment medical services for people without easy access to specialists.
This paper tackles the problem of diagnosing common dermatological diseases by developing a deep-learning classifier, achieving approximately 80% accuracy compared to 57% for primary care doctors, to provide preliminary information for patients.
This paper introduces a deep-learning based efficient classifier for common dermatological conditions, aimed at people without easy access to skin specialists. We report approximately 80% accuracy, in a situation where primary care doctors have attained 57% success rate, according to recent literature. The rationale of its design is centered on deploying and updating it on handheld devices in near future. Dermatological diseases are common in every population and have a wide spectrum in severity. With a shortage of dermatological expertise being observed in several countries, machine learning solutions can augment medical services and advise regarding existence of common diseases. The paper implements supervised classification of nine distinct conditions which have high occurrence in East Asian countries. Our current attempt establishes that deep learning based techniques are viable avenues for preliminary information to aid patients.