## 2 Related Work and Theoretical Foundation
### Current State of Machine Learning in Skin Disease Detection  
In recent years, with the progress of machine learning technology, the expectations of artificial intelligence have been increasing, and research on its applications in dermatology has actively progressed [24]. In the literature, several studies proposed shallow learning-based skin disease solutions. For example, one study [25] used the artificial neural network to detect melanoma cancer from color images. The model used a discriminant feature to  
Figure 1: Existing skin disease detection devices: (**a**) dermoscopy; (**b**) dermalite; (**c**) laser microscopy. diagnose the tumor shape, categorize the melanoma skin disease into three main categories, and separate melanoma-like diseases. Another study [26] used the fuzzy c means region segmentation approach based on color discrimination. In this approach, a histogram (two-dimensional) is calculated along with the principal components and the Gaussian low-pass filter analysis. Similarly, an extreme machine learning-based approach was proposed in [27] to detect skin cancer. This approach requires an extensive process for feature extraction and texture analysis; thus, it is not feasible for clinical trials. In contrast, some other studies worked on optimized feature extraction processes and determined that the spread of chronic skin diseases in different regions may lead to severe consequences. For example, [28] proposed a support vector machine-based model that automatically detects eczema and determines its severity on the basis of three different stages, i.e., effective segmentation, extraction of optimized features (color, texture, and borders), and severity of the disease itself. Another impressive attempt was made by [29] to extract more appropriate features. This study used the joint power of computer vision and machine learning to detect six types of skin disease. However, these studies were not adequate when addressing similar features across multiple diseases (e.g., some types of eczema show similar features to cancer); as such, wrong classifications may be obtained. The feature similarity issue can be overcome using low-level feature extraction techniques. For that reason, deep learning approaches (such as the convolutional neural network) are more desirable due to their pixel-level feature extraction process. Recently, several studies used deep learning-based techniques to classify similar features more accurately. In [30], a hybrid approach was designed by using a combination of shallow learning- and deep learning-based pretrained models, such as AlexNet and support vector machine. A recent study [31] highlighted the enormous potential of deep understanding to detect skin diseases with human-like diagnosis accuracy or better. Furthermore, this study urged the utilization of deep learning-based real-time intelligent healthcare systems for clinical utilization. The current approaches are only based on the batch learning approach and are static; thus, they do not welcome any future change (i.e., the model would need to be retrained if any changes are required). However, clinical procedures require a continuous upgradation to increase their accuracy and add new kinds of skin disease, rendering the current static deep learning approaches inapplicable. Accordingly, new adaptive mechanisms are required to ensure adaptability with high classification accuracy. After an extensive analysis of the literature, it can be safely concluded that health practitioners usually apply manual or computer vision-based tools for the diagnosis of skin diseases, which may lead to misinterpretation and a longer analysis time. Existing devices such as laser microscopy and multimeter wave devices are only applicable in particular situations and are not soon expected to be implemented in clinical practices. On the contrary, dermoscopy and dermalite are majorly used clinically, but they require extensive health practitioner input to observe skin diseases.