Skin Cancer accounts for one-third of all diagnosed cancers worldwide. The prevalence of skin cancers have been rising over the past decades. In recent years, use of dermoscopy has enhanced the diagnostic capability of skin cancer. The accurate diagnosis of skin cancer is challenging for dermatologists as multiple skin cancer types may appear similar in appearance. The dermatologists have an average accuracy of 62% to 80% in skin cancer diagnosis. The research community has been made significant progress in developing automated tools to assist dermatologists in decision making. In this work, we propose an automated computer-aided diagnosis system for multi-class skin (MCS) cancer classification with an exceptionally high accuracy. The proposed method outperformed both expert dermatologists and contemporary deep learning methods for MCS cancer classification. We performed fine-tuning over seven classes of HAM10000 dataset and conducted a comparative study to analyse the performance of five pre-trained convolutional neural networks (CNNs) and four ensemble models. The maximum accuracy of 93.20% for individual model amongst the set of models whereas maximum accuracy of 92.83% for ensemble model is reported in this paper. We propose use of ResNeXt101 for the MCS cancer classification owing to its optimized architecture and ability to gain higher accuracy.

皮肤癌占全球所有诊断出的癌症的三分之一。在过去的几十年中，皮肤癌的发病率一直在上升。近年来，皮肤镜的使用增强了皮肤癌的诊断能力。对于皮肤科医生而言，皮肤癌的准确诊断具有挑战性，因为多种皮肤癌类型可能看起来相似。皮肤科医生在皮肤癌诊断中的平均准确度为62％至80％。在开发自动化工具以协助皮肤科医生进行决策方面，研究界已取得重大进展。在这项工作中，我们提出了一种用于多类皮肤（MCS）癌症分类的自动化计算机辅助诊断系统，具有极高的准确性。拟议的方法优于专家皮肤科医生和当代深度学习方法进行MCS癌症分类。我们对HAM10000数据集的七类进行了微调，并进行了比较研究，以分析五个预训练卷积神经网络（CNN）和四个集成模型的性能。在该组模型中，单个模型的最大准确性为93.20％，而整体模型的最大准确性为92.83％。我们建议将ResNeXt101用于MCS癌症分类，因为它具有优化的架构和获得更高准确度的能力。在该组模型中，单个模型的最大准确性为93.20％，而整体模型的最大准确性为92.83％。我们建议将ResNeXt101用于MCS癌症分类，因为它具有优化的架构和获得更高准确度的能力。在该组模型中，单个模型的最大准确性为93.20％，而整体模型的最大准确性为92.83％。我们建议将ResNeXt101用于MCS癌症分类，因为它具有优化的架构和获得更高准确度的能力。