Optimized lung cancer detection by amended whale optimizer and rough set theory

IF 3 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC International Journal of Imaging Systems and Technology Pub Date : 2023-04-11 DOI:10.1002/ima.22888

Zuzheng Chang, Dragan Rodriguez

引用次数: 0

Abstract

The current paper proposes a new hierarchical procedure for efficient diagnosis of lung cancer computed tomography (CT) images. Here, after noise removal based on median filtering, a contrast enhancement based on general histogram equalization (GHE) has been utilized. Then, a modified version of K-means clustering has been used for the area of interest segmentation in the CT images. The major characteristics of the segmented images have been selected during an optimization technique and the outputs are injected into an optimized radial basis function (RBF) network for the final classification. Optimization in the classification stage and feature selection is by an improved metaheuristic technique, called Amended Whale Optimization Algorithm was proposed. The designed method is then applied to “The RIDER Lung CT” database and its achievements are validated by several latest techniques to show its higher efficacy.

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用改进的鲸鱼优化器和粗糙集理论优化癌症检测

本文提出了一种新的分级方法，用于癌症计算机断层扫描（CT）图像的有效诊断。这里，在基于中值滤波的噪声去除之后，已经使用了基于通用直方图均衡（GHE）的对比度增强。然后，将K均值聚类的改进版本用于CT图像中的感兴趣区域分割。分割图像的主要特征已经在优化技术期间被选择，并且输出被注入到优化的径向基函数（RBF）网络中用于最终分类。在分类阶段和特征选择的优化是通过一种改进的元启发式技术，称为修正的鲸鱼优化算法。然后将所设计的方法应用于“RIDER Lung CT”数据库，并通过几种最新技术对其成果进行了验证，以显示其更高的疗效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.