Breast cancer: A hybrid method for feature selection and classification in digital mammography

IF 3 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2023-04-25 DOI:10.1002/ima.22889

Shankar Thawkar, Vijay Katta, Ajay Raj Parashar, Law Kumar Singh, Munish Khanna

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引用次数: 2

Abstract

In this article, a hybrid approach based on the Whale optimization algorithm (WOA) and the Dragonfly algorithm (DA) is proposed for breast cancer diagnosis. The hybrid WOADA method selects features based on the fitness value. These features are used to predict the breast masses as benign or malignant using artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) as classifiers. The proposed solution is evaluated by using 651 mammograms. The results demonstrate that the WOADA technique outperforms the basic WOA and DA approaches. The accuracy of the suggested WOADA algorithm is 97.84%, with a Kappa value of 0.9477 and an AUC value of 0.972 ± 0.007 for the ANN classifier. Likewise, the ANFIS classifier achieved 98.00% accuracy with a Kappa value of 0.96 and an AUC value of 0.998 ± 0.001. In addition, the viability of the hybrid WOADA technique was evaluated on four benchmark datasets and then compared with four state-of-the-art algorithms and published approaches.

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癌症乳腺：数字乳腺摄影中特征选择和分类的混合方法

本文提出了一种基于Whale优化算法（WOA）和Dragonfly算法（DA）的乳腺癌症诊断混合方法。混合WOADA方法基于适应度值来选择特征。使用人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）作为分类器，将这些特征用于预测乳腺肿块为良性或恶性。所提出的解决方案通过使用651张乳房X光片进行评估。结果表明，WOADA技术优于基本的WOA和DA方法。建议的WOADA算法的准确率为97.84%，Kappa值为0.9477，AUC值为0.972 ± 对于ANN分类器为0.007。同样，ANFIS分类器实现了98.00%的准确率，Kappa值为0.96，AUC值为0.998 ± 0.001.此外，在四个基准数据集上评估了混合WOADA技术的可行性，然后将其与四种最先进的算法和已发表的方法进行了比较。

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.