Convolutional neural networks (CNNs) are a branch of deep learning which have been turned into one of the popular methods in different applications, especially medical imaging. One of the significant applications in this category is to help specialists make an early detection of skin cancer in dermoscopy and can reduce mortality rate. However, there are a lot of reasons that affect system diagnosis accuracy. In recent years, the utilization of computer-aided technology for this purpose has been turned into an interesting category for scientists. In this research, a meta-heuristic optimized CNN classifier is applied for pre-trained network models for visual datasets with the purpose of classifying skin cancer images. However there are different methods about optimizing the learning step of neural networks, and there are few studies about the deep learning based neural networks and their applications. In the present work, a new approach based on whale optimization algorithm is utilized for optimizing the weight and biases in the CNN models. The new method is then compared with 10 popular classifiers on two skin cancer datasets including DermIS Digital Database Dermquest Database. Experimental results show that the use of this optimized method performs with better accuracy than other classification methods.
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机译：卷积神经网络（CNNS）是深度学习的分支，该分支已成为不同应用中的流行方法之一，尤其是医学成像。该类别中的一个重要应用是帮助专家早期发现皮肤病皮肤癌症，可以降低死亡率。但是，有很多原因会影响系统诊断精度。近年来，为此目的的计算机辅助技术的利用变成了科学家的一个有趣的类别。在该研究中，应用了Meta-heuristic优化的CNN分类器，用于预先训练的网络模型，用于视觉数据集，目的是对皮肤癌图像进行分类。然而，有关于优化神经网络的学习步骤的不同方法，少数关于基于深度学习的神经网络及其应用的研究。在本作工作中，基于鲸鲸优化算法的新方法用于优化CNN模型中的重量和偏差。然后将新方法与10种皮肤癌数据集合的10种普遍的分类器进行比较，包括Dermis Digital Database Dermest数据库。实验结果表明，使用这种优化方法的使用比其他分类方法更好。
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