Munir Khushboo, Elahi Hassan, Ayub Afsheen, Frezza Fabrizio, Rizzi Antonello
Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
Department of Mechanical and Aerospace Engineering (DIMA), Sapienza University of Rome, Via Eudossiana 18, 00184 Rome, Italy.
Cancers (Basel). 2019 Aug 23;11(9):1235. doi: 10.3390/cancers11091235.
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann's machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements.
在本文中，我们首先描述癌症诊断领域的基础知识，其中包括癌症诊断的步骤以及医生使用的典型分类方法，向读者提供癌症分类技术的历史概念。这些方法包括不对称性、边界、颜色和直径（ABCD）法、七点检测法、门齐斯法和模式分析。医生在癌症诊断中经常使用这些方法，尽管它们在获得更好的诊断性能方面并非十分有效。此外，考虑到所有类型的受众，还讨论了基本评估标准。这些标准包括受试者工作特征曲线（ROC曲线）、ROC曲线下面积（AUC）、F1分数、准确率、特异性、敏感性、精确率、骰子系数、平均准确率和杰卡德指数。以前使用的方法被认为效率低下，因此需要更好、更智能的癌症诊断方法。人工智能与癌症诊断作为一种定义更好诊断工具的方式正受到关注。特别是，深度神经网络可成功用于智能图像分析。本研究提供了这种机器学习在医学成像上的工作基本框架，即预处理、图像分割和后处理。本文的第二部分描述了不同的深度学习技术，如卷积神经网络（CNN）、生成对抗模型（GAN）、深度自动编码器（DAN）、受限玻尔兹曼机（RBM）、堆叠自动编码器（SAE）、卷积自动编码器（CAE）、循环神经网络（RNN）、长短期记忆（LTSM）、多尺度卷积神经网络（M-CNN）、多实例学习卷积神经网络（MIL-CNN）。对于每种技术，我们都提供了Python代码，以便感兴趣的读者能够就他们自己的诊断问题对引用的算法进行实验。本文的第三部分汇编了针对不同类型癌症成功应用的深度学习模型。考虑到手稿篇幅，我们将讨论限制在乳腺癌、肺癌、脑癌和皮肤癌。这篇文献综述的目的是为选择从事深度学习和人工神经网络在癌症诊断中的应用研究的人员提供关于最新成果的从零开始的知识。
