## 2 Related Work
### Classification
[30] introduced an ensemble deep transfer learning model for breast cancer classification and used DenseNet201 and ResNet152V2 for transfer learning. As increasing interests of a transformer model, ref. [31] presented a transformer with localization U-Net model for pancreatic cancer classification, and [32] introduced ViT and variant CNN models including pre-trained VGG16 and ResNet50 for breast cancer classification. Also, refs. [33; 34] studied transformer-based models for fundus disease and prostate cancer classifications. For further study, ref. [35] introduced variant transformer models, such as scale-aware transformer, and [36] utilized encoded multi-scale features with a transformer to improve the classification performance. In COVID-19 classification, ref. [37] introduced a deep transfer learning model combined with machine learning algorithms for predicting COVID-19 disease with chest CT images. Refs. [38; 39] also introduced ViT [40] using a performer and feature aggregation method by transformer. Therefore, because of the introduction of the transformer model, various transformer-based models have been studied to improve classification performance in the medical domain. Ref. [41] proposed a novel image pre-processing method that utilizes histogram equalization and contrast limited adaptive histogram equalization to create a three-channel image representation, and the method is evaluated using pre-trained models. Ref. [42] experimented with variant CNN models pre-trained on the ImageNet dataset on a chest X-ray dataset to classify COVID-19. Some studies have classified the types of lesions. Ref. [43] introduced a regularizer that can be used for the size and number of CNN filters and experimented on the International Skin Imaging Collaboration (ISIC) 2018 dataset to classify a combination of two classes (namely, nevus vs. melanoma) out of seven rather than classifying all seven lesions. Ref. [44] applied a pre-trained CNN model with data augmentation on the ISIC 2018 and 2019 dataset. Ref. [45] presented a ResNet50 model with curriculum learning and momentum contrastive learning and evaluated the method on the ISIC 2019 dataset. Ref. [46] proposed classifying with SVM by re-extracting only the features of the last fully connected layer using various models, such as AlexNet, VGG16, and ResNet18, which were pre-trained with ImageNet. Because the ISIC 2016 and 2017 datasets were used in the experiment, it had a limitation of only two classes. Furthermore, ref. [47] presented a deep belief network using semi-supervised learning that could use both labeled and unlabeled data. The authors proposed an SVM learning method to generate advice weights from misclassified data and applied them to a test set. Although the authors introduced self-supervised learning for classification, no pretext task was given to learn self-information in the image. Rather, an unlabeled image was simply used to adjust the parameter space of the model. In addition, this study had a limitation of only two classes, melanoma and benign. Refs. [48,49] presented a method to transfer knowledge from a pre-trained CNN and used the model to classify seven lesion classes on the HAM10000 dataset. Moreover, ref. [50] suggested a ResNet50 model with task-specific layers for skin lesion classification and demonstrated that learning different tasks together in the same biomedical imaging modalities has the potential to train deep models effectively. As for recent works, ref. [51] introduced Xception and ShuffleNet models using transfer learning and utilized butterfly optimization to improve feature selection. Ref. [52] proposed a Skin-net using residual learning and cross-channel correlation.