# Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
## 1 Introduction  
Cancer can cause death if not diagnosed and treated in a timely fashion and can start almost anywhere in the human body. Skin cancer is a common type of cancer, as more than three million Americans are diagnosed with skin cancer each year ([[https://www.skincancer.org/skin-cancer-information/skin-cancer-facts](https://www.skincancer.org/skin-cancer-information/skin-cancer-facts)]([https://www.skincancer.org/skin-cancer-information/skin-cancer-facts](https://www.skincancer.org/skin-cancer-information/skin-cancer-facts)), accessed on: 24 October 2021). If skin cancer is diagnosed early, it can usually be treated. There are eight categories of skin cancers: melanoma (MEL), melanocytic nevi (NV), basal cell carcinoma (BCC), benign keratosis lesions (BKL), actinic keratosis (AK), dermatofibroma (DF), squamous cell carcinoma (SCC), and vascularl Lesions (VASC) [1]. MEL is the most dangerous typeof cancer, as it spreads to other organs very rapidly. It develops in body cells called melanocytes. MEL is not common as compared to other categories of skin cancer. NV are pigmented moles and vary in different colors of skin tones. It mostly develops during childhood and early adult life as the number of moles increases up to the age of 30 to 40. Thereafter, the number of naevi tends to decrease. BCC develops in cells of the skin called basal cells. The basal cell performs the functionality of producing new skin cells as old ones die off. AK is a pre-cancer that develops on skin affected by chronic exposure to ultraviolet (UV) rays. BKL is one of the common benign neoplasms of the skin. DF occur at all ages and in people of every ethnicity. It is not clear if DF is a reactive process or a neoplasm [2]. The lesions are made up of proliferating fibroblasts. Vascular lesions are relatively common abnormalities of the skin and underlying tissues. SCC is the most accruing form of skin cancer after melanoma and usually results from exposure to UV rays. In literature, machine learning approaches such as support vector machine (SVM) [2], neural networks [3], naive Bayes classifier [4], and decision trees [5] have been used for skin cancer classification. The problem with machine learning approaches is the requirement of human-engineered features. In the last decade, deep learning approaches, such as convolutional neural networks (CNN) became popular due to their ability with regard to automatic feature extraction [6; 7; 8; 9], and have been extensively used in research [10; 11; 12; 13]. [PERSON] et al. [14] worked on skin cancer classification using deep CNN. [PERSON] et al. in [15] performed melanoma cancer classification with the dermoscopy images using CNN. The technique has an accuracy of 81.3% on the International Skin Imaging Cancer (ISIC) archive dataset. [PERSON] et al. [16] carried out pigmented skin lesion classification using the clinical images and faster region-based CNN. The classification accuracy of the method was compared with the ten-board certified dermatologist diagnosis accuracy. [PERSON] et al. [11] performed multiclass skin cancer classification using dermoscopy images with the different variants of CNN. [PERSON] et al. in [17] developed a probabilistic model to achieve the better performance of a fully convolutional network-based deep learning system for the analysis and segmentation of skin lesion images. The probabilistic model achieved an accuracy of 98%. Recently, researchers have proposed ensemble methods to enhance classification performance [18; 19; 20]. [PERSON] et al. in [21] developed ensemble model using ResNet-152 [22], DenseNet-161 [22], SE-ResNeXt-101 [23], and NASNet [23] for the classification of seven classes of skin cancer using the ISIC dataset and achieved an accuracy of 93%. The ensemble is a machine learning method that combines the decision of several individual learners to increase classification accuracy [24].