ABSTRACT

The patients having malignant breast tumours if detected in early stage have a better chance of survival. It is observed that the analysis of the texture features of the breast thermograms helps in providing the right information for diagnosis to a greater extent. In this study, the breast thermograms of 56 subjects having temperature recordings available at Database Mastology Research (DMR), visual labs are considered. Further, the texture features in the Gray level Run Length Matrix (GLRLM) and Gray level Co-occurrence Matrix (GLCM) are extracted from these images. The correlation of features gives a linear relationship between the variables that help to analyse the quantitative relationship between the variables. The features are selected by using unsupervised feature reduction techniques, i.e. Principal Component Analysis (PCA) and Autoencoder (AE). The features selected are observed to be relevant in detecting the abnormality between healthy and unhealthy breast. Different classifiers viz. support vector machine, decision tree, random forest, K-NN, linear Regression, and fuzzy logic are then applied to the selected features for detecting the presence of malignancy in breast. Among all the classifiers, Random Forest (RF) with PCA has been observed to yield an accuracy of 95.45% in classifying the benign and malignant tumours.

摘要

早期发现患有恶性乳腺肿瘤的患者有更好的生存机会。据观察，对乳房热像图纹理特征的分析有助于在更大程度上为诊断提供正确的信息。在这项研究中，考虑了 56 名受试者的乳房热像图，这些受试者的温度记录可在数据库乳腺学研究 (DMR) 视觉实验室获得。此外，从这些图像中提取灰度游程矩阵（GLRLM）和灰度共生矩阵（GLCM）中的纹理特征。特征的相关性给出了变量之间的线性关系，有助于分析变量之间的定量关系。通过使用无监督的特征减少技术来选择特征，即 主成分分析 (PCA) 和自动编码器 (AE)。所选择的特征被观察到与检测健康和不健康乳房之间的异常相关。不同的分类器即。然后将支持向量机、决策树、随机森林、K-NN、线性回归和模糊逻辑应用于所选特征，以检测乳房中是否存在恶性肿瘤。在所有分类器中，随机森林 (RF) 与 PCA 已被观察到在分类良性和恶性肿瘤时产生 95.45% 的准确率。然后将模糊逻辑应用于所选特征以检测乳房中是否存在恶性肿瘤。在所有分类器中，随机森林 (RF) 与 PCA 已被观察到在分类良性和恶性肿瘤时产生 95.45% 的准确率。然后将模糊逻辑应用于所选特征以检测乳房中是否存在恶性肿瘤。在所有分类器中，随机森林 (RF) 与 PCA 已被观察到在分类良性和恶性肿瘤时产生 95.45% 的准确率。