结合长城构建算法的双层卷积神经网络CT扫描诊断与手术选择。

IF 2.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Harish Kumar, Anuradha Taluja, Elangovan Muniyandy, Srinivas Kolli
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引用次数: 0

摘要

世界上最常见的癌症之一是肾癌(KC)。精确的诊断受到许多变量的影响,如肿瘤的大小或体积、癌症的类型和分期等,这对于治疗肾癌患者至关重要。在这项工作中,使用可访问的KiTS21对比增强CT扫描数据集和患者的相关数据,研究了两种主要类型的肾癌:正常和异常。许多这些技术显示出较差的准确性,这引起了对其效率和可靠性的怀疑。为了克服这些限制,本文提出了一种双层卷积神经网络与长城构建算法(DDCNN-GWCA)的结合。混合快速常规双边滤波器通过使用KiTS21数据集减少噪声,同时保留关键信息,提高了预处理数据的质量。采用实用量子k均值聚类进行分割,提高了检测效率和准确率。q值正则化变压器(Q-value regularization Transformer, QT)是一种将变压器功率与q值正则化相结合来捕获相关特征的特征提取方法。采用双层卷积神经网络的多层结构进行分类,对分类对象进行分类。长城构建算法是一种创新的优化技术,它对双层卷积神经网络(DDCNN)的超参数进行优化,以确保其性能的增强。它在KiTS21数据集上获得了98.9%的分数。这些结果证明了该策略优于现有方法的能力,并为肾癌诊断的重大进展开辟了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Kidney cancer diagnosis and surgery selection by double decker convolutional neural network from CT scans combined with great wall construction algorithm

One of the most prevalent cancers in the world is kidney cancer (KC). A precise diagnosis, which is influenced by a number of variables, such as the size or volume of the tumor, the types and stages of the cancer, etc., is essential for the treatment of patients with kidney cancer. In this work two main types of kidney cancer: normal and abnormal, using the accessible KiTS21 dataset of contrast-enhanced CT scans and associated data from patients. Many of these techniques show poor accuracy, which raises doubts regarding their efficiency and dependability. To overcome these limitations, this paper presents the use of a double-decker convolutional neural network with the great wall construction algorithm (DDCNN-GWCA). Hybrid quick conventional bilateral filter improves the quality of pre-processed data by reducing noise while preserving crucial information by using the KiTS21 dataset. Practical Quantum K-Means Clustering is used for segmentation to improve detection efficiency and accuracy. The Q-value Regularized Transformer (QT) is a feature extraction method that combines the power of transformers with Q-value regularization to capture the relevant features. A Double-Decker Convolutional Neural Network's multi-layered architecture is used for classification to identify the classes. The Great Wall Construction Algorithm is an innovative optimization technique that optimizes the hyperparameters of the Double Decker Convolutional Neural Network (DDCNN), ensuring enhanced performance. It obtained scores of 98.9% for the KiTS21 dataset. These results demonstrate the strategy's ability to outperform existing methods and open the way for major advances in the diagnosis of kidney cancer.

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来源期刊
Abdominal Radiology
Abdominal Radiology Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
5.20
自引率
8.30%
发文量
334
期刊介绍: Abdominal Radiology seeks to meet the professional needs of the abdominal radiologist by publishing clinically pertinent original, review and practice related articles on the gastrointestinal and genitourinary tracts and abdominal interventional and radiologic procedures. Case reports are generally not accepted unless they are the first report of a new disease or condition, or part of a special solicited section. Reasons to Publish Your Article in Abdominal Radiology: · Official journal of the Society of Abdominal Radiology (SAR) · Published in Cooperation with: European Society of Gastrointestinal and Abdominal Radiology (ESGAR) European Society of Urogenital Radiology (ESUR) Asian Society of Abdominal Radiology (ASAR) · Efficient handling and Expeditious review · Author feedback is provided in a mentoring style · Global readership · Readers can earn CME credits
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