针对 Biglycan 生物标记图像的乳腺癌检测特征和分类技术比较分析。

IF 2.2 4区 医学 Q3 ONCOLOGY
Jumana Ma'touq, Nasim Alnuman
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引用次数: 0

摘要

背景:乳腺癌(BC)被认为是世界上发病率最高的癌症。乳腺癌的早期诊断可使患者得到更好的护理和治疗,从而降低患者死亡率。由于乳腺组织的复杂性和病变表现的多样性,即使是经验丰富的放射科医生也很难对乳腺病变进行识别和分类:这项工作旨在研究在 336 张 Biglycan 生物标记图像中准确检测乳腺癌的适当特征和分类技术:方法:从 Mendeley 数据网站(资源库名称:Biglycan 乳腺癌数据集)检索 Biglycan 生物标记图像。根据形状特征(即哈里斯点和最小特征值(MinEigen)点)、频域特征(即二维傅里叶变换和小波变换)和统计特征(即直方图),提取并比较了五个特征。使用了六种不同的常用分类算法,即 K 近邻(k-NN)、奈夫贝叶斯(NB)、伪线性判别分析(pl-DA)、支持向量机(SVM)、决策树(DT)和随机森林(RF):灰度图像的直方图显示,k-NN(97.6%)、SVM(95.8%)和 RF(95.3%)分类器的性能最佳。此外,在五种特征中,灰度直方图特征在所有分类器中都达到了最佳准确率,最高准确率为 97.6%,而小波特征在大多数分类器中都提供了可喜的准确率(最高 94.6%):机器学习在估计癌症方面表现出很高的准确性,这种技术可以帮助医生分析常规医学影像和活检样本,从而改善早期诊断和风险分层。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparative analysis of features and classification techniques in breast cancer detection for Biglycan biomarker images.

Background: Breast cancer (BC) is considered the world's most prevalent cancer. Early diagnosis of BC enables patients to receive better care and treatment, hence lowering patient mortality rates. Breast lesion identification and classification are challenging even for experienced radiologists due to the complexity of breast tissue and variations in lesion presentations.

Objective: This work aims to investigate appropriate features and classification techniques for accurate breast cancer detection in 336 Biglycan biomarker images.

Methods: The Biglycan biomarker images were retrieved from the Mendeley Data website (Repository name: Biglycan breast cancer dataset). Five features were extracted and compared based on shape characteristics (i.e., Harris Points and Minimum Eigenvalue (MinEigen) Points), frequency domain characteristics (i.e., The Two-dimensional Fourier Transform and the Wavelet Transform), and statistical characteristics (i.e., histogram). Six different commonly used classification algorithms were used; i.e., K-nearest neighbours (k-NN), Naïve Bayes (NB), Pseudo-Linear Discriminate Analysis (pl-DA), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF).

Results: The histogram of greyscale images showed the best performance for the k-NN (97.6%), SVM (95.8%), and RF (95.3%) classifiers. Additionally, among the five features, the greyscale histogram feature achieved the best accuracy in all classifiers with a maximum accuracy of 97.6%, while the wavelet feature provided a promising accuracy in most classifiers (up to 94.6%).

Conclusion: Machine learning demonstrates high accuracy in estimating cancer and such technology can assist doctors in the analysis of routine medical images and biopsy samples to improve early diagnosis and risk stratification.

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来源期刊
Cancer Biomarkers
Cancer Biomarkers ONCOLOGY-
CiteScore
5.20
自引率
3.20%
发文量
195
审稿时长
3 months
期刊介绍: Concentrating on molecular biomarkers in cancer research, Cancer Biomarkers publishes original research findings (and reviews solicited by the editor) on the subject of the identification of markers associated with the disease processes whether or not they are an integral part of the pathological lesion. The disease markers may include, but are not limited to, genomic, epigenomic, proteomics, cellular and morphologic, and genetic factors predisposing to the disease or indicating the occurrence of the disease. Manuscripts on these factors or biomarkers, either in altered forms, abnormal concentrations or with abnormal tissue distribution leading to disease causation will be accepted.
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