Department of Biomedical Engineering, School of Applied Medical Sciences, German Jordanian University, Amman, Jordan.
Physiotherapy Department, Faculty of Allied Medical Sciences, Isra University, Amman, Jordan.
Cancer Biomark. 2024;40(3-4):263-273. doi: 10.3233/CBM-230544.
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.
This work aims to investigate appropriate features and classification techniques for accurate breast cancer detection in 336 Biglycan biomarker images.
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).
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%).
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.
乳腺癌（BC）被认为是世界上最普遍的癌症。早期诊断乳腺癌可以使患者得到更好的治疗，从而降低患者的死亡率。由于乳腺组织的复杂性和病变表现的多样性，即使是有经验的放射科医生，乳腺病变的识别和分类也具有挑战性。
本研究旨在探讨在 336 个 Biglycan 生物标志物图像中准确检测乳腺癌的合适特征和分类技术。
从 Mendeley Data 网站（存储库名称：Biglycan 乳腺癌数据集）检索 Biglycan 生物标志物图像。基于形状特征（即 Harris 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 5 个特征。使用了 6 种常用的分类算法，即 K-近邻（k-NN）、朴素贝叶斯（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）。
灰度图像的直方图在 k-NN（97.6%）、SVM（95.8%）和 RF（95.3%）分类器中表现最好。此外，在 5 个特征中，灰度直方图特征在所有分类器中都具有最佳的准确性，最高可达 97.6%，而小波特征在大多数分类器中提供了有希望的准确性（最高可达 94.6%）。
机器学习在估计癌症方面具有很高的准确性，这种技术可以帮助医生分析常规医学图像和活检样本，以提高早期诊断和风险分层。
