The effect of autoencoders over reducing the dimensionality of a dermatology data set

The effect of using autoencoders for dimensionality reduction of a medical data set is investigated. A stack of two autoencoders has been trained for popular benchmark medical data set for dermatological disease diagnosis. The improvement of the presented approach has been visualized by the Principal Component Analysis method. Results shows that the use of a autoencoders significantly improves the accuracy of dermatological disease diagnosis.