# Computer-Aided Diagnosis of Skin Diseases using Deep Neural Networks
## 1 Introduction  
Deep Learning (DL) [1] is a branch of Artificial Intelligence (AI) in which a computer algorithm analyses raw data and automatically learns discriminatory features needed for recognizing hidden patterns in them. Over the last decade, this field has witnessed striking advances in the ability of DL-based algorithms to analyse various types of data, especially images [2] and natural language [3]. The most common DL models are trained using supervised learning, in which datasets are composed ofinputs (e.g., dermoscopic images of skin diseases) and corresponding target output labels (e.g., diagnoses or skin disease classes such as 'benign' or'malignant'). Healthcare and medicine can greatly benefit from recent advances in image classification and object detection [4], particularly those medical disciplines in which diagnoses are primarily based on detection of morphologic changes such as pathology, radiology, ophthalmology and dermatology etc. In such medical domains, digital images are captured and provided to DL algorithms for Computer-Aided Diagnosis (CAD). These advance algorithms have already made their mark on automated detection of tuberculosis [5], breast malignancy [6], glaucoma [7], diabetic retinopathy [8] and serious brain findings such as stroke, haemorrhage, and mass effects [9].  
Large scale manual screening for diseases is exhaustively laborious, extremely protracted, and severely susceptible to human predisposition and fatigue. Since manual diagnosis may also be affected by physicians' level of experience and different dermoscopic algorithms in which they are formally trained, multiple experts might disagree on their diagnosis for a certain condition [10,11]. Additionally, due to physicians' subjective judgements, manual diagnosis is hardly reproducible [12]. On the other hand, CAD can provide swift, reliable and standardized diagnosis of various diseases with consistency and accuracy. CAD can also afford the opportunity of efficient and cost-effective screening and prevention of advanced tumour diseases to people living in rural or remote areas where expert dermatologists are not readily available.  
Most of publicly available datasets for clinical or dermoscopic images like Interactive Atlas of Dermoscopy [13], Dermofit Image Library [14], Global Skin Atlas, MED-NODE [15] and PH2 [16] etc. contain only a few hundreds to a couple of thousand images. [PERSON] et al. [17] reported that around 78% of the studies they surveyed used datasets smaller than 1000 images and the study using the largest dataset had 2430 images. Therefore, most of existing works on CAD of skin diseases use either private or very small publicly available datasets. Additionally, these studies usually render overwhelming focus on only binary or ternary classification of skin diseases and not much attention is paid to multi-class classification to explore the full potential of DL. Therefore, such studies act merely as a proof-of-concept for the efficacy of AI in dermatology.  
In this work, we extend previous works by showing that DL model are fairly capable of recognising hundreds of skin lesions, and therefore should be capitalized to their full extent. We trained many state-of-the-art DL models for classification of skin diseases using two of the largest publicly available datasets, namely DermNet and ISIC Archive (2018 version). We also employed non-visual data in the form of disease taxonomy to improve our classification results and show that DL can process and utilize multi-model input for better classification performance.