  The diagnosis of the Erythemato-squamous disease (ESD) is accepted as a
difficult problem in dermatology. ESD is a form of skin disease. It generally
causes redness of the skin and also may cause loss of skin. They are generally
due to genetic or environmental factors. ESD comprises six classes of skin
conditions namely, pityriasis rubra pilaris, lichen planus, chronic dermatitis,
psoriasis, seboreic dermatitis and pityriasis rosea. The automated diagnosis of
ESD can help doctors and dermatologists in reducing the efforts from their end
and in taking faster decisions for treatment. The literature is replete with
works that used conventional machine learning methods for the diagnosis of ESD.
However, there isn't much instances of application of Deep learning for the
diagnosis of ESD. In this paper, we propose a novel hybrid deep learning
approach i.e. Derm2Vec for the diagnosis of the ESD. Derm2Vec is a hybrid deep
learning model that consists of both Autoencoders and Deep Neural Networks. We
also apply a conventional Deep Neural Network (DNN) for the classification of
ESD. We apply both Derm2Vec and DNN along with other traditional machine
learning methods on a real world dermatology dataset. The Derm2Vec method is
found to be the best performer (when taking the prediction accuracy into
account) followed by DNN and Extreme Gradient Boosting.The mean CV score of
Derm2Vec, DNN and Extreme Gradient Boosting are 96.92 percent, 96.65 percent
and 95.80 percent respectively.
