# A Multiclassification Model for Skin Diseases Using Dermatoscopy Images with Inception-v2
## 2 Related Work
Additionally, a mobile Android application was developed that employs the DenseNet-169 architecture to determine whether a skin lesion is benign or malignant. Ultimately, the DenseNet-169 model exhibited the highest accuracy (92.25%), surpassing other models such as ResNet-50, Vgg-16, and Inception. The DenseNet-121 model attained the second highest accuracy. For mobile applications, the DenseNet-169 model demonstrated an accuracy of 91.10%. [PERSON] et al. [33] proposed a deep convolutional neural network (CNN)-based automatic classifier for triple classification of dermatologic diseases. The primary objective was to propose a deep CNN that is more straightforward than other techniques in order to identify melanoma skin tumours in an efficient manner. The ISIC datasets were employed to acquire dermoscopic images for this study, which included several image datasets, such as ISIC 2016, ISIC 2017 and ISIC 2020. The accuracy of the classifiers for melanoma, basal cell carcinoma, and nevi on the ISIC 2016, 2017, and 2020 datasets was 81.41%, 88.23%, and 90.42%, respectively. [PERSON] et al. [34] employed a convolutional neural network (CNN) model and a modified residual network (ResNet-50) on the HAM10000 dataset. This categorisation employed an inhomogeneous skin cancer sample. Initially, the image quality was enhanced using ESRGAN, and then data enhancement was employed to address the category imbalance. The results obtained using the CNN and ResNet-50 models exhibited accuracies of 86% and 85.3%, respectively. [PERSON] et al. [35] employed the EfficientNetsB0-B7 model on the HAM10000 dermoscopic image dataset. The dataset comprises 10,015 images, categorised according to seven distinct types of skin cancer: actinic keratosis (AKIEC), dermatofibrosarcoma (DF), non-vascular lesions (NVs), BCC, MEL, benign keratosis lentits (BKL), and vascular skin lesions (VASCs). Among the eight models, the EfficientNetsB4 model exhibited the highest level of accuracy, with a Top-1 and Top-2 accuracy of 87% and an F1-score of 87.91%. [PERSON] et al. [36] employed the ISIC 2018 dataset and, utilising an ensemble of the GoogleNet and Vgg-16 models, augmented the dataset and normalised its colours to construct an integrated method subsequently provided. The accuracy of the method was 84.1%. In light of the ongoing advancements in dermoscopic image classification algorithms, this paper presents a summary of two key points. 1. The efficacy of feature extraction and classification of dermoscopic images using convolutional neural networks is contingent upon the performance of the network. However, the current algorithms tend to utilise a single convolutional neural network, which has a relatively low classification accuracy rate. 2. Classification studies of dermoscopic images are predominantly binary, particularly in the context of melanoma recognition. Furthermore, studies investigating multiclassification problems are scarce and of limited scope. Consequently, there is considerable scope for research in the area of dermoscopic image multiclassification. In this paper, a deep convolutional neural network (CNN) model is employed for the classification of dermoscopic images representing seven categories of skin diseases. To enhance the dataset, data augmentation techniques are utilised to expand the sample size, balance the distribution of the data points, and optimise the dataset through hair removal. Furthermore, the focal loss function is applied to improve the classification accuracy for challenging categories.