Pourasad Yaghoub, Zarouri Esmaeil, Salemizadeh Parizi Mohammad, Salih Mohammed Amin
Department of Electrical Engineering, Urmia University of Technology (UUT), Urmia 57166-93188, Iran.
School of Electrical Engineering, Electronic Engineering, Iran University of Science and Technology-IUST, Tehran 16846-13114, Iran.
Diagnostics (Basel). 2021 Oct 11;11(10):1870. doi: 10.3390/diagnostics11101870.
Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor's location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.
乳腺癌是全球女性主要的死亡原因之一。早期发现这种疾病有助于减少过早死亡的人数。本研究旨在设计一种基于超声图像识别和诊断乳腺肿瘤的方法。为此，已采用六种技术来检测和分割超声图像。使用分形方法提取图像特征。此外，使用k近邻、支持向量机、决策树和朴素贝叶斯分类技术对图像进行分类。然后，设计卷积神经网络（CNN）架构直接基于超声图像对乳腺癌进行分类。所提出的模型在训练集上的准确率达到99.8%。关于测试结果，这种诊断验证的灵敏度为88.5%。基于本研究的结果，可以得出结论，所提出的具有高潜力的CNN算法可用于从超声图像诊断乳腺癌。第二个提出的CNN模型可以识别肿瘤的原始位置。结果显示，在高性能区域中92%的图像的曲线下面积（AUC）高于0.6。所提出的模型可以通过形态学操作作为后处理算法来识别肿瘤的位置和体积。这些发现也可用于监测患者并防止感染区域的扩大。
