Journal of Circuits, Systems and Computers ( IF 1.5 ) Pub Date : 2022-09-24 , DOI: 10.1142/s0218126623500640 R. Vinoth 1 , R. Sasireka 2
Ultrasound imaging is commonly used to diagnose internal anomalies. Imaging for abnormality detection is a challenging process in today’s world. Even though there is an advancement in technology, tele-radiographers face difficulty in the accurate diagnosis of abnormalities. In order to resolve this issue, tele-radiology has paved a new way for doctors around the world to access the Internet to share the radiological images from one location to another. But frequent online access is one of the bottleneck issues. In order to overcome this drawback, Computer Assisted Diagnosis (CAD) is preferred in this proposed study and it uses VIRTEX-6 FPGA to clearly identify abnormality in the platform and also manual control is minimized in this condition. The proposed algorithm includes five steps: pre-processing, segmentation, feature extraction, selection and classification. The classification is performed using the Iterative K-Nearest Neighbor (IKNN) classifier based on the selected features. Unlike popular KNN, the proposed IKNN algorithm performs the similarity measurement on selective neighbors for a number of times where the number of neighbors has been dynamically selected at each iteration. Also, at each iteration, the method would select a subset of features in a random way. For the features selected and with the neighbors selected, the method computes the similarity value of Hist-sim which is being measured according to the features selected from the histogram features where the method computes the Haralick similarity with the features selected from the Haralick features. Using the features selected, the method computes the value of cumulative class drive similarity (CCDS). At each iteration the class with maximum similarity is selected and finally, the class being selected for the most number of times is selected as a result of classification. This improves the performance of classification. While comparing with the existing algorithms such as Support Vector Machine (SVM) with the linear, Radial Basis Function (RBF) and polynomial kernels, greater accuracy is achieved via IKNN classification. The specificity is found to be 95, 80 and 75 for normal, cystic and stone kidneys.
中文翻译:
具有 IKNN 相关 FPGA 异常分类的超声肾脏图像
超声成像通常用于诊断内部异常。在当今世界,用于异常检测的成像是一个具有挑战性的过程。尽管技术有所进步,但远程放射技师在准确诊断异常方面仍面临困难。为了解决这个问题,远程放射学为世界各地的医生访问互联网以将放射图像从一个位置共享到另一个位置铺平了一条新途径。但频繁的在线访问是瓶颈问题之一。为了克服这个缺点,计算机辅助诊断 (CAD) 在这项拟议的研究中是首选,它使用 VIRTEX-6 FPGA 来清楚地识别平台中的异常,并且在这种情况下手动控制也被最小化。所提出的算法包括五个步骤:预处理,分割,特征提取,选择和分类。分类是使用基于所选特征的迭代 K 最近邻 (IKNN) 分类器执行的。与流行的 KNN 不同,所提出的 IKNN 算法对选择性邻居执行多次相似性测量,其中在每次迭代时动态选择邻居的数量。此外,在每次迭代中,该方法将以随机方式选择一个特征子集。对于选择的特征和选择的邻居,该方法根据从直方图特征中选择的特征计算正在测量的 Hist-sim 的相似度值,其中该方法计算与从 Haralick 特征中选择的特征的 Haralick 相似度。使用选择的特征,该方法计算累积类驱动相似性 (CCDS) 的值。在每次迭代中选择具有最大相似性的类,最后选择被选择次数最多的类作为分类结果。这提高了分类的性能。与现有的线性、径向基函数(RBF)和多项式核的支持向量机(SVM)等算法相比,精度更高通过IKNN 分类。发现正常、囊性和结石肾的特异性为 95、80 和 75。