## 7 Overview of the Major Applications of AI in Dermatology
### Core Applications
Comparing the proposed method to existing recent work on the same dataset revealed that the proposed method achieved greater accuracy with minimal loss and errors. In [185], the authors propose hybrid systems that leverage the benefits of fused CNN models. CNN models receive dermoscopy images from the ISIC 2019 dataset after the Geometric Active Contour (GAC) algorithm has segmented the lesion area and isolated it from healthy skin. An ANN and random forest (RF) receive CNN features that have been fused and accurately classify them. Using hybrid models, CNN-ANN and CNN-RF, the first methodology involves analysing the areas of skin lesions and identifying their types early on. CNN models (AlexNet, GoogLeNet, and VGG16) receive only the lesion area and generate maps with a considerable depth of features. Then, PCA is used to reduce the depth of the feature maps, which are then classified by the ANN and RF. The second method involves analysing the areas of skin lesions and obtaining an early diagnosis of their type using hybrid CNN-ANN and CNN-RF models based on the characteristics of fused CNN models. Notably, the features of the CNN models were serially integrated after principal component analysis (PCA) reduced their dimensions. Hybrid models based on fused CNN features demonstrated promising results in diagnosing dermatoscopic images from the ISIC 2019 dataset and differentiating skin cancer from other skin lesions. AlexNetGoogleNetVGG16-ANN achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%. Although AI algorithms show promise in detecting skin cancer, they should be viewed as supplementary tools rather than replacements for clinical expertise. The interpretation and judgment of dermatologists, combined with AI assistance, can result in improved patient outcomes and the more efficient management of skin cancer. AI systems can analyse patient symptoms and medical records to provide automated diagnoses and treatment recommendations for dermatological conditions. For example, scientists have created and trained a deep neural network algorithm capable of differentiating eczema from other infectious skin diseases, including classifying rare skin lesions with direct clinical implications. This study's diagnostic capabilities informed treatment recommendations, such as the use of topical corticosteroids versus antiseptics [186]. It demonstrates the potential of AI for individualised treatment planning for eczema patients. These systems utilise machine learning techniques to learn from vast amounts of data, thereby improving the accuracy over time. Research from 2018 [187] identified 43 skin cancer smartphone apps. Most involved remote consultation, monitoring, information, education, skin self-examination, or the prevention of UV radiation exposure. The studies that have proposed DNN-based smartphone apps for skin disease diagnosis include [188; 189; 190; 191; 192]. The study in [189] used the MobileNet architecture. MobileNet has fewer parameters than other networks. MobileNet architectures reduce first-layer computations with depth-wise separable convolutions. This architecture supports CPU-only applications. The model from [189] used a second version of MobileNet and transfer learning to diagnose early-stage skin cancer, with 91.33% accuracy. This model was used because it had 90.1% top-5 ImageNet accuracy and was smaller. Training this model is straightforward and requires only a few GPUs. This model can also be customised for low-spec smartphones. It was demonstrated that the proposed application could accurately classify lesions as either benign or malignant. They used dermoscopy images, smartphone photos, and histology slides (microscope images). DermNet datasets, ISIC Archive, and the Dermofit Image Library provided these images. Photographs are variable due to factors such as the lighting, angle, zoom, and region of interest, making classification difficult. Classifier generalisation becomes crucial.