# Skin Cancer Disease Detection Using Transfer Learning Technique
## 2 Related Work  
Various techniques have been proposed for melanoma classification in the previous few decennaries. Most methods [43; 44; 45] used image processing techniques to extract features and then fed them into a classification technique. [PERSON] et al. [46] presented a detection and classification technique between melanoma and nevi. At first, the author applied a Gaussian filter to remove noise. K-mean clustering was used for lesion segmentation. Then, textural and color features were extracted using a hybrid super feature vector. After that, support vector machines (SVMs) were applied for classification. The proposed methodology obtained 96% accuracy on the ERMIS dataset. [PERSON] et al. [47] presented a new technique based on combining deep learning (DL) and handcrafted features. The developed method obtained 87.8% on the ISIC challenge dataset and 98% accuracy on the Ph2 dataset. [PERSON] et al. [48] used an approach based on feature similarity measurement, and then SVM was used for classification. [PERSON] et al. [49] presented a five-layer system known as \"DermoDeep\" to differentiate between nevi and melanoma. This method integrated visual features and a five-layer model to achieve the best classification results. [PERSON] et al. [8] extracted three types of features (texture, geometrical properties, and color) and selected optimal features using ant-colony-based segmentation. Then ANN was used for classification. [PERSON] et al. in [50] proposed an approach in which textual features were extracted, and then SVM was implemented as a classifier. The presented model achieved 90% accuracy on 227 images. [PERSON] et al. [51] used image enhancement techniques for extracting ROIs. After that, SVM was used for the classification of the pre-processed images. The attained accuracy was 87.2%. [PERSON] et al. [52] introduced a method to enhance the images for extracting ROIs and used a deep residual model to classify images. The obtained accuracy of the proposed system was 85.5%. Recently, researchers have been working on melanoma classification by using deep learning models. [PERSON] et al. [53] developed a new method depending on deep CNN and feature encoding techniques (FV encoding) to create more meaningful features for accurate melanoma recognition. The developed model was trained on the ISIC 2016 dataset and archived with a 86.54% accuracy. [PERSON] et al. [54] proposed a deep CNN architecture to classify melanoma dermoscopy images into benign skin lesions and malignant melanoma. The presented approach was evaluated on the ISIC-archive repository. The proposed approach gained 91.97% sensitivity, 84.76% accuracy, and 78.71% specificity. [PERSON] et al. [7] used a classification method based on the ensemble model. [PERSON] et al. [55] developed an ensemble model based on three classifiers to classify mole images in non-melanomas and melanomas. [PERSON] et al. [56] presented a new method based on spiking neural networks with time-dependent spike plasticity. [PERSON] et al. [57] implemented a deep CNN architecture for melanoma classification. The presented method was tested on three different datasets. [PERSON] et al. [58] used a CNN-based method known as CNN malignant lesions detection (CMLD). The developed model achieved 90.14% and 90.58% accuracy on MED-NODE and Dermofit datasets. [PERSON] et al. [59] presented a technique for detecting skin diseases as an initial stage and classifying skin cancer using deep networks. [PERSON] et al. [60] presented a deep neural network-based model called Nasnet Mobile to detect melanoma. The presented technique was evaluated on the HAM10000 dataset. Various augmentation techniques were used to tackle the problem of imbalanced classes. The proposed model obtained the accuracy with the Nasnet-Mobile network was 89.20% without data augmentation and 97.90% with data augmentation. [PERSON] et al. [61] used a pre-trained architecture named ResNet50 to classify the skin lesion as melanoma or nevi.