# A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
## 2 Related Work  
The classification of early skin diseases is dependent upon manual extraction of features based on colour, texture, and other characteristics [20]. In the majority of cases, experts rely on a subjective evaluation of the characteristics of the image lesions in question. This is due to the fact that the process of labelling is inherently subjective, and visual evaluation of the human eye is not sufficiently accurate to be relied upon in this context. Furthermore, lesions are often subject to changes in their borders and the presence of hairs, amongst other factors [21]. Conventional edge processing techniques are inadequate for the effective capture of minute texture details in dermatoscopic images. To address this challenge, researchers have devised novel methodologies. [PERSON] et al. proposed the use of texture and colour feature descriptors, modelled using the bag-of-words approach, which led to an enhancement in accuracy by 3% and 4%, respectively, on the PHP and Dermofit datasets [22]. In their study, [PERSON] et al. employed decision trees for the purpose of feature selection, with the objective of identifying the optimal combination of features to the recognition of skin cancer [23]. [PERSON] et al. employed an artificial neural network to extract the features, which were then integrated with shape and colour features derived from traditional methods to enhance the robustness of the extracted features [24]. [PERSON] et al. extracted pertinent features, including colour and texture, from the lesion area through the process of image segmentation [25]. Nevertheless, these methodologies are inadequate for effectively recognising dermoscopic images that exhibit high interclass similarity and high intraclass diversity. The field of dermatology is characterised by a multitude of classification and recognition methods, many of which are based on convolutional neural networks (CNNs). The advent of CNNs has led to the establishment of CNN-based classification and recognition as a mainstream approach due to the exceptional feature extraction capabilities of neural networks. The growing popularity of deep learning has resulted in the gradual evolution of mainstream feature extraction methods from manual feature selection to convolutional neural networks (CNNs) and their variants [26]. These include the early AlexNet and Inception series of models, the VGG and ResNet [27] series of models for deeper networks, and the lightweight networks applicable to mobile, such as MobileNet and ShuffleNet [28]. These networks employ ImageNet as a pre-trained dataset, which is subsequently utilised as a backbone structure for classification tasks on other datasets or for other deep learning tasks via transfer learning. In contrast to traditional classification methods, where features are selected by human beings, these methods learn features directly from images through deep networks for classification, thereby enhancing the feature extraction capability to a certain extent and, consequently, the classification accuracy [29]. [PERSON] et al. [30] employed two novel hybrid convolutional neural network (CNN) models with a support vector machine (SVM) at the output layer to categorise dermoscopic images as benign or malignant cancers. A combination of parameters extracted from the initial CNN model and the second CNN model was provided to the SVM classifier. The hybrid DenseNet-201 and the MobileNet model demonstrated an accuracy of 88.02%, while the hybrid DenseNet-201 and the ResNet-50 model exhibited an accuracy of 87.43%. [PERSON] et al. [31] applied deep peaking neural networks to 3670 images of melanoma and 3323 images of non-melanoma lesions from the ISIC 2019 dataset. With the peaked Vgg-13 model, they obtained an accuracy of 89.57% and an F1-score of 90.07%, which were higher than those obtained with the Vgg-13 and AlexNet models. [PERSON] et al. [32] constructed 11 convolutional neural network (CNN) architectures for the classification of various skin lesions using the HAM10000 dataset.