## 2 Related Works
### DL Approaches
#### 2.2.1 Non-Segmentation DL Approaches  
[PERSON] et al. [44] applied six open datasets for lesion image classification. They used Google Inception-v4 and ResNet-101 architectures to detect seborrheic keratosis, malignant melanoma, and nevus. They also confirmed that combined datasets increase training data and improve classification accuracy. [PERSON] et al. [45] introduced Resnet-152 for the lesions image classification. The lesions include squamous cell carcinoma, basal cell carcinoma, actinic keratosis, intraepithelial carcinoma, and malignant melanoma. The factors that reduce the recognition accuracy of skin lesions are image contrast and ethnicity.  
[PERSON] et al. [46] used Inception v3 architecture to classify the lesion into three groups. The method first distinguishes between benign and malignant and then separates seborrheic keratoses and keratinocyte carcinoma types. It also recognizes nevi and malignant melanomas.  
[PERSON] et al. [47] classified skin lesions into 21 classes and introduced a four-level lesion identify method. Skin pictures are arranged in four levels using the GoogleLeNet model. Benign and malignant samples are classified first, followed by recognition of melanocytic and epithelial. The method also identified seborrheic keratosis, actinic keratosis, Bowen, and basal cell carcinoma lesions. [PERSON] et al. [18] inserted an attention layer at the end of ResNet architecture and created new attention residual network. They classified seborrheic keratosis, melanoma, and nevus lesion.  
[PERSON] et al. [48] introduced a new method to recognize skin lesions using the fine-tuned pretrained network. They first applied AlexNet, VGG16, and ResNet to extract skin image features from the last fully connected layers. Then, they applied SVM to fuse these extracted features. [PERSON] [49] presented a method that fuses the outcome probabilities of the four CNN models: AlexNet, GoogLeNet, VGGNet, and ResNet. The study proposed the four fusion techniques to identify skin lesions: seborrheic keratosis, melanoma, and nevus. The sum fusion provides better results than other rules (simple majority voting, product fusion, and the maximal probability).  
[PERSON] and [PERSON] [50] presented the classification of the dermatological picture using various CNN models, such as VGG19, VGG16, Inception, Xception, DenseNet, and ResNet. They applied these models to extract features of two different inputs: the original skin and segmented images. The proposed method combined these two extracted features to predict skin lesions. Numerical results confirm that ensemble CNN has a better performance than single CNN in skin classification. Consequently, ensemble architecture outperforms individual architecture.  
[PERSON] et al. [51] proposed an aggregate DL technique to match three lesion classes: seborrheic keratosis, melanoma, and nevus. They used two binary classifiers on the basis of ResNet-50 architecture. The first classifier distinguishes between melanoma and another lesion. Meanwhile, the second classifier identifies the relationship between seborrheic keratosis and another lesion. The proposed method recognizes the skin lesion images by combining the output probabilities of these two binary classifiers. [PERSON] and [PERSON] [52] combined two ResNet architectures and obtained the features at the fully connected layer of each ResNet model. They combined the extracted features to classify skin lesions into seborrheic keratosis, melanoma, and nevus.