# Computer-Aided Diagnosis of Skin Diseases using Deep Neural Networks
## 4 Discussion  
Automated diagnosis of skin diseases has enjoyed much attention from researchers for quite some time now. However, most of these researches confine themselves to only binary or ternary classification [38; 39; 40; 41; 42; 43] even when large number of classes are available [44]. The importance of early detection of melanoma is understandable given the growing risk it poses to the patient's survival with every passing day. However, there are thousands of other skin diseases [24] that might not be as fatal  
Figure 3: Examples of correctly and incorrectly classifies diseases. (**a**) Correctly classified ACROS in DermNet (**b**) Correctly Classified NAIL in DermNet (**c**) Correctly Classified SEB in DermNet (**d**) Correctly Classified VASC in ISIC (**e**) CEL Misclassified as ACROS in DermNet (**f**) Correctly Classified AKIEC in ISIC (**g**) BKL Misclassified as MEL in ISIC (**h**) NV Misclassified as VASC in ISIC. All Images are resized to fit in square windows. Figure 2: Confusion Matrix showing number of correctly classified and misclassified images per class in ISIC Archive-2018. as melanoma but have an enormous impact on a patient's quality of life. DL is extremely competent to take on hundreds of classes simultaneously, as evident by our results. We believe that this is right time to harvest the potential of DL to its full extent and start conducting real impactful research that can actually translate into industry standard solution for automated skin disease diagnosis on a larger scale. These solutions can have a far-reaching social impact by not only helping dermatologist with their diagnosis in a clinical setup but also providing an economical and efficient initial screening for underprivileged people in both developed and developing countries. Another consideration in terms of application of DL in dermatology is that many researchers either use private datasets or public datasets with their own choice of train/test splits (although randomly taken) and number of classes. For this reason, there is little common ground, and often times no ground at all, to compare various classification methods--as also noted by [PERSON] et al. [45]. This issue of non-comparability can be resolved by collecting and maintaining a standardized publicly available large dataset with explicitly specified train/test splits and standard performance metrics for benchmarking. Notwithstanding that some public datasets, like ISIC Challenges datasets, do provide this beforehand train/test split but their size is normally small and task is usually restricted to binary or ternary classification. Any research on such small datasets cannot be reliably generalized and although the results are publishable, they cannot be used as stepping stone for practical applications of AI in real-world diagnosis. On the other hand, large public datasets normally have a lot of noise, images with disgracefully low resolution or are watermarked. Significant useful information required for fine-grained classification of seemingly similar diseases is lost in such low resolution or watermarked images. Additionally, non-visual metadata, like medical history, is not usually available with medical image datasets. However, this additional information could be pivotal for confident and accurate diagnosis. We were able to utilize disease taxonomy for DermNet dataset and improve our results by 2.5% (refer to Table A1). If multi-model datasets are curated and provided publicly, AI can surely leverage additional information to improve its classification performance. While understanding and interpreting results of any AI-based classifier it is important to realize that accuracy, or even sensitivity and specificity, might not portray the complete picture of a model's performance. That is why Area Under Receive Operating Characteristic (ROC) Curve (AUC) is also reported along with other performance metrics.