# Classification of Skin Lesions Using Weighted Majority Voting Ensemble Deep Learning
## 1 Introduction  
Malignant skin lesions such as melanoma manifest a serious health risk with increasing incidence. The ability of melanoma to rapidly grow and widely proliferate to other parts of the human body is a unique characteristic that makes it one of the deadliest skin cancer diseases [1]. It is a fatal malignant skin tumor resulting from the improper synthesis of melanin because of unpredictable disorders in the melanocytic cell [2]. The good news though is that fatality of skin lesions can be prevented to a large degree if the disease is detected early before proliferation. The high mortality rate associated with melanoma makes it necessary to detect the disease at its early stages to improve health treatment as quickly as possible [3]. Statistics have indicated that over 9000 deaths are recorded yearly from melanoma infection, which warrants the necessity to provide a sustainable and easily reproducible method of early detection [4, 5, 6, 7]. It has been judged to be the fifth most common skin cancer occurring among males, seventh in females, and second among young adults ranging from 15-29 years of age [8, 9, 10]. Moreover, given that melanoma accounts for 75-79% of skin cancer-related deaths, early detection has been proposed by medical practitioners as one of the vivacious keys to preventing untimely death associated with the disease [8, 11]. This is one of the important reasons that manifold researchers are interestedin obtaining accurate automated systems that can assist in the early detection and diagnosis of the disease [12]. The diagnostic procedure for skin lesions often relies heavily on dermatologists who use visual pattern recognition with the aid of dermoscopic devices to identify lesions through rules such as asymmetry, border irregularity, color variation, the diameter of the lesion, a 7-point checklist, and Menzies score. However, the associated complexity, high subjectivity, and a great deal of experience in interpreting dermoscopic evaluation results present a serious concern [8; 13; 14]. Recent reports have noted the poor prognosis from expert dermatologists that can sometimes lead to expensive and unnecessary excision [15]. These challenges necessitate a reliable automated system to assist with the second opinion [4]. There are numerous methods in the literature to automatically determine whether a skin lesion is malignant or not, and most studies have reported promising results. The application of machine learning methods is widely accepted in recent times as having the potential capacity to act as a second opinion for the automatic classification of skin lesions [16]. Machine learning (ML) applies artificial intelligence (AI) methods to provide computing systems with the capability to learn the experience of performing non-trivial tasks. The frequently used ML methods include decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), and multiple regression analysis (MRA). Specifically, ANN is a widely known ML method that tends to simulate biological neural networks using collections of connected nodes to model biological neurons in the human brain. There are copious applications of ANNs for classifying skin lesions with encouraging results [17; 18; 19]. Deep neural networks (DNNs) have recently evolved as variations of ANNs with consideration for more dense hidden layers to improve classification performance. The process of training DNNs for learning to perform a non-trivial assignment is termed deep learning (DL). Frequently used DNNs include convolution neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs). The CNN applications in computer vision have been widely lauded with increasing success, because of their ability to solve signal translation problems by convolving each input signal with a kernel detector to train and learn spatial relations among image pixels [20; 21].