A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease

TL;DR

This paper introduces Derm2Vec, a hybrid deep learning model combining Autoencoders and DNNs, for improved diagnosis of Erythemato-squamous disease, outperforming traditional methods with high accuracy.

Contribution

The paper presents a novel hybrid deep learning approach, Derm2Vec, specifically designed for diagnosing ESD, which has not been extensively explored with deep learning before.

Findings

Derm2Vec achieved a mean CV score of 96.92%

DNN achieved a mean CV score of 96.65%

Extreme Gradient Boosting scored 95.80%

Abstract

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.