Interdisciplinary Sciences: Computational Life Sciences ( IF 3.9 ) Pub Date : 2023-03-28 , DOI: 10.1007/s12539-023-00560-4 Volkan Göreke 1
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Nodules of thyroid cancer occur in the cells of the thyroid as benign or malign types. Thyroid sonographic images are mostly used for diagnosis of thyroid cancer. The aim of this study is to introduce a computer-aided diagnosis system that can classify the thyroid nodules with high accuracy using the data gathered from ultrasound images. Acquisition and labeling of sub-images were performed by a specialist physician. Then the number of these sub-images were increased using data augmentation methods. Deep features were obtained from the images using a pre-trained deep neural network. The dimensions of the features were reduced and features were improved. The improved features were combined with morphological and texture features. This feature group was rated by a value called similarity coefficient value which was obtained from a similarity coefficient generator module. The nodules were classified as benign or malignant using a multi-layer deep neural network with a pre-weighting layer designed with a novel approach. In this study, a novel multi-layer computer-aided diagnosis system was proposed for thyroid cancer detection. In the first layer of the system, a novel feature extraction method based on the class similarity of images was developed. In the second layer, a novel pre-weighting layer was proposed by modifying the genetic algorithm. The proposed system showed superior performance in different metrics compared to the literature.
Graphical Abstract
中文翻译：
一种基于深度学习的新型 CADx 架构，用于使用超声图像对甲状腺结节进行分类
甲状腺癌结节以良性或恶性形式出现在甲状腺细胞中。甲状腺超声图像主要用于甲状腺癌的诊断。本研究的目的是介绍一种计算机辅助诊断系统，该系统可以使用从超声图像收集的数据对甲状腺结节进行高精度分类。子图像的采集和标记由专科医生进行。然后使用数据增强方法增加这些子图像的数量。使用预先训练的深度神经网络从图像中获得深层特征。特征的尺寸被减小并且特征被改进。改进的特征与形态和纹理特征相结合。该特征组通过从相似系数生成器模块获得的称为相似系数值的值来评级。使用多层深度神经网络和采用新颖方法设计的预加权层，将结节分类为良性或恶性。在这项研究中，提出了一种用于甲状腺癌检测的新型多层计算机辅助诊断系统。在系统的第一层，开发了一种基于图像类相似性的新颖特征提取方法。在第二层中，通过修改遗传算法提出了一种新颖的预加权层。与文献相比，所提出的系统在不同指标上表现出优越的性能。
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