Zhang Long, Gao Hong Jie, Zhang Jianhua, Badami Benjamin
Department of medical equipment, People's hospital of Zhengzhou University, Zhengzhou, 450001, China.
Institute of Medical Engineering Technology and Data Mining, Zhengzhou University, Zhengzhou, 450001, China.
Open Med (Wars). 2020 Jan 13;15:27-37. doi: 10.1515/med-2020-0006. eCollection 2020.
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.
卷积神经网络（CNNs）是深度学习的一个分支，已成为不同应用中流行的方法之一，尤其是在医学成像领域。这类应用中的一个重要应用是帮助专家在皮肤镜检查中早期发现皮肤癌，并可降低死亡率。然而，有很多因素会影响系统诊断的准确性。近年来，为此目的使用计算机辅助技术已成为科学家们感兴趣的一个领域。在本研究中，一种元启发式优化的CNN分类器应用于视觉数据集的预训练网络模型，以对皮肤癌图像进行分类。然而，关于优化神经网络学习步骤有不同的方法，而基于深度学习的神经网络及其应用的研究很少。在目前的工作中，一种基于鲸鱼优化算法的新方法被用于优化CNN模型中的权重和偏差。然后将该新方法与包括DermIS数字数据库和Dermquest数据库在内的两个皮肤癌数据集上的10种流行分类器进行比较。实验结果表明，使用这种优化方法的准确率优于其他分类方法。
