一种混合深度学习方法用于红斑-鳞状疾病的诊断
A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease
摘要：
红斑鳞状上皮病（ESD）的诊断一直是皮肤科的难题。ESD是一种皮肤病。它通常会导致皮肤发红，也可能导致皮肤脱落。这通常是由遗传或环境因素造成的。ESD包括六类皮肤病，即毛发红色糠疹、扁平苔藓、慢性皮炎、牛皮癣、皮脂腺性皮炎和玫瑰糠疹。ESD的自动诊断可以帮助医生和皮肤科医生减少他们的工作量，并更快地做出治疗决定。文献中充满了使用传统机器学习方法诊断ESD的作品。然而，将深度学习应用于ESD诊断的实例并不多。在本文中，我们提出了一种新的混合深度学习方法，即Derm 2 Vec用于ESD的诊断。Derm 2 Vec是一个混合深度学习模型，由自动编码器和深度神经网络组成。我们还将传统的深度神经网络（DNN）用于ESD的分类。我们将Derm 2 Vec和DNN以及其他传统的机器学习方法应用于真实世界的皮肤病学数据集。Derm 2 Vec方法被认为是最好的执行者（当考虑到预测精度时），其次是DNN和极端梯度提升。Derm 2 Vec、DNN和极端梯度提升的平均CV得分分别为96.92%、96.65%和95.80%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
