Breast cancer diagnosis based on mammary thermography and extreme learning machines

Abstract Introduction Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes. Results Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.

摘要 引言
乳腺癌是女性最常见的恶性肿瘤，也是全球范围内女性癌症相关死亡的主要诱因之一。早期检出与干预治疗是实现临床治愈的核心路径。乳腺热成像（mammary thermography）作为辅助成像技术，在乳腺病学（Mastology）领域的应用愈发广泛，可用于早期检出乳腺病变。当前已有研究探索将其作为筛查检查手段以识别各类乳腺疾病。本研究旨在探究不同分类方法在将热成像图像归类为特定乳腺病变类型时的分类表现。

方法
为评估本研究提出的方案，我们基于人工神经网络（artificial neural networks）、决策树、贝叶斯分类器（Bayesian classifiers）以及哈拉利克特征与泽尼克特征构建了分类器。本研究的图像数据库采集自伯南布哥联邦大学附属医院（University Hospital of the Federal University of Pernambuco），所纳入的热成像图像经临床诊断划分为囊肿、恶性病变与良性病变三类。本研究采用泽尼克矩与哈拉利克特征作为分类属性。

结果
极限学习机（Extreme Learning Machines, ELM）与多层感知机（Multilayer Perceptron networks, MLP）被证实为用于热成像图像乳腺病变分类的高效分类器。以75%的数据库样本用于模型训练时，最高分类准确率可达73.38%，对应的卡帕指数（Kappa index）为0.6007。该结果对应的分类灵敏度为78%，特异度为88%，系统整体分类效能达83%。

结论
极限学习机凭借较低的计算成本与优异的鲁棒性，展现出用于热成像图像乳腺病变鉴别分类的良好应用前景。