# Multiclass Skin Cancer Classification Using Ensemble of Fine-Tuned Deep Learning Models
## 2 Related Work  
Skin cancer is usually diagnosed with the physical examination of the skin or with the help of biopsy. The detection of skin cancer through the physical examination requires a great degree of experience and expertise, and biopsy-based examination is a tedious and time consuming task as it also requires expert pathologists. Currently, the macroscopic and dermoscopy images are used by the dermatologist during the detection procedure of skin cancer. But even with the dermoscopy images, accurate skin cancer detection is a challenging task, as multiple skin cancers may appear similar in initial appearance. Furthermore, even the expert dermatologists have limited studies and exposure experience to different types of skin cancer through their lifetimes. Expert dermatologists have a skin cancer detection accuracy of 62% to 80% [33; 34]. The reports on diagnostic accuracy of clinical dermatologists have shown 62% accuracy with the clinical experience of three to five years. However, the dermatologists with more than 10 years of experiernce have a diagnostic accuracy of 80%, and diagnostic performance falls even more for dermatologists with the less experience [34]. Therefore, dermoscopy in the hands of an inexperienced dermatologist may reduce diagnostic accuracy [33; 35; 36]. In early 1980s computer-aided diagnosis (CAD) systems were developed to assist the dermatologist to meet the challenges faced during the process of diagnosis [37]. Initially, CADs were developed using a dermoscopy image for binary classification of melanoma and benign [37]. Since then, much research has been carried out to solve this challenging problem. Several studies [37; 38; 39] have been performed using the manual evaluation methods developed using the ABCD method proposed by [PERSON] et al. in [40]. Moreover, machine learning classifiers have been developed with handcrafted features. These classifiers include support vector machine (SVM) [2], naive Bayes [4], k-nearest neighbor [41], logistic regression [42], and artificial neural networks [3]. But the existence of high intra-class and low interclass variations in MEL have caused the unsatisfactory performance of handcrafted features-based cancer detection [43]. With the advent of deep learning, CNN caused a breakthrough for the solution of many problems, including skin cancer classification. CNN gave higher detection accuracy and it also reduced the burden of hand-crafted feature extraction by automatically extracting the feature [44]. As CNNs require huge datasets for better feature leaning at abstract levels [45], transfer learning has been introduced to meet the limitation of huge dataset requirements [20; 32], where a model trained for a task is reused for another task. [PERSON] et al. proposed a skin lesion detection technique by developing a transfer learning- based AlexNet model for the classification of three skin lesions in [46]. To develop the proposed method, data augmentation is applied to enhance the dataset to achieve an accuracy of 98.61%. [PERSON] et al. developed a skin cancer detection technique using datasetconsisting of 1300 images to construct a linear classifier using the features extracted by the CNN in [47]. The technique does not require preprocessing or skin lesion segmentation. They carried out classifications of five and 10 classes and achieved an accuracy of 85.8% and 81.9%, respectively. In [32], a novel CNN architecture has been developed. The architecture relies on multiple tracts to perform the skin lesion classification. The authors used a pretrained CNN for single resolution and retained it for multi-resolution on publicly available datasets and obtained an accuracy of 79.15% for the ten classes. In [48], [PERSON] et al. developed a skin lesion classification approach using deep convolutional neural networks (DCNN) to classify benign and malignant skin lesions.