# Computer-Aided Diagnosis of Skin Diseases using Deep Neural Networks
## 3 Results  
### Results on DermNet  
As DermNet provides the opportunity to leverage taxonomical relationship among various diseases, therefore, for 23-ary classification we conducted our experiments in two ways. In the first experiment (Exp-1), we trained our networks on 23 classes and inferred on 23 classes. This is the most prevalent approach. We achieved \(77.53\pm 0.64\%\) Top-1 accuracy and \(93.87\pm 0.37\%\) Top-5 accuracy with \(97.60\pm 0.15\%\) Area Under the Curve (AUC) using ensemble of four best models. In second experiment (Exp-2) we made use of additionally given ontology in the dataset. We trained our network on 622 classes but inferred on 23 classes only. The use of this disease ontology information translates into incorporation of expert knowledge into the network. We implemented this by summing the predictions of all sub-classes to calculate the prediction of respective super-class. This approach gave us noticeable boost in our classifiers' performance. We got \(79.94\pm 0.45\%\) Top-1 accuracy, \(95.02\pm 0.15\%\) Top-5 accuracy and \(98.07\pm 0.07\%\) AUC using ensemble. Top-N accuracy indicates the capability of a classifier to predict correct class in first \(N\) attempts. This metric gives a deeper insight into the classifier's learning and discriminating ability. Our results,of Exp-2 for example, show that the model was able to predict the correct diagnosis out of 23 possible diseases in first attempt with almost 80% accuracy. However, when allowed to make 5 most probable predictions about a given image, the classifier achieved more than 95% accuracy. This means that even when the first prediction of the classifier is wrong, the would-be correct prediction is high on the list of next four predictions. Table 3 shows detailed performance metrics of 23-ary classification in both experiments. Accuracies and AUC scores of individual classifiers for Exp-1 and Exp-2 are given in Table A1 in Appendix A. Figure 1 shows that many reciprocatory misclassifications in Exp-1, like between Eczema (Abbreviated as ECZ in Figure 1) and Psoriasis Lichen Planus (PSO) and between Actinic Keratosis BCC (AKBCC) and Seborrheic Keratosis (SEB), are corrected to a large extent in Exp-2 by utilizing taxonomical relationship among diseases. We not only performed classification for 23 super-classes but also took a step forward and tried to classify all 622 unique sub-classes as well. We obtained \(66.74\pm 0.64\%\) Top-1 accuracy and \(86.26\pm 0.54\%\) Top-5 accuracy with \(98.34\pm 0.09\%\) AUC. Small values of standard deviation in all of these results signify the stability and consistency of our classifier's performance. Previous works on DermNet have generally opted for a subset of 23 super-classes for classification. However, [PERSON] [33] chose to classify all 23 classes and reported best Top-1 accuracy of 73.1% and Top-5 accuracy of 91% on 1000 randomly chosen test images. [PERSON] et al. [34] reported Top-1 accuracy of 60% on 24 classes (they split \"Melanoma and Melanocytic Nevi\" into malignant and benign classes). They picked only 100 examples of each class for their test set. To the best of our knowledge, previously the classification task with highest number of classes using DermNet has been performed by [PERSON] et al. [35]. They performed 200-ary classification and obtained highest Mean Class Accuracy