Kayode Aderonke Anthonia, Akande Noah Oluwatobi, Adegun Adekanmi Adeyinka, Adebiyi Marion Olubunmi
Computer Science Department, Landmark University, Omu-Aran, Kwara State, Nigeria.
Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria.
Med Devices (Auckl). 2019 Aug 12;12:275-284. doi: 10.2147/MDER.S206973. eCollection 2019.
Breast cancer remains a serious public health problem that results in the loss of lives among women. However, early detection of its signs increases treatment options and the likelihood of cure. Although mammography has been established to be a proven technique of examining symptoms of cancer in mammograms, the manual observation by radiologists is demanding and often prone to diagnostic errors. Therefore, computer aided diagnosis (CADx) systems could be a viable alternative that could facilitate and ease cancer diagnosis process; hence this study.
The inputs to the proposed model are raw mammograms downloaded from the Mammographic Image Analysis Society database. Prior to the classification, the raw mammograms were preprocessed. Then, gray level co-occurrence matrix was used to extract fifteen textural features from the mammograms at four different angular directions: θ={0°, 45°, 90°, 135°}, and two distances: D={1,2}. Afterwards, a two-stage support vector machine was used to classify the mammograms as normal, benign and malignant.
All of the 37 normal images used as test data were classified as normal (no false positive) and all 41 abnormal images were correctly classified to be abnormal (no false negative), meaning that the sensitivity and specificity of the model in detecting abnormality is 100%. After the detection of abnormality, the system further classified the abnormality on the mammograms to be either "benign" or "malignant". Out of 23 benign images, 21 were truly classified as benign. Also, out of 18 malignant images, 17 were truly classified to be malignant. From these findings, the sensitivity, specificity, positive predictive value, and negative predictive value of the system are 94.4%, 91.3%, 89.5%, and 95.5%, respectively.
This article has further affirmed the prowess of automated CADx systems as a viable tool that could facilitate breast cancer diagnosis by radiologists.
乳腺癌仍然是一个严重的公共卫生问题，导致女性死亡。然而，早期发现其症状可增加治疗选择和治愈的可能性。尽管乳房X线摄影已被确立为检查乳房X线照片中癌症症状的可靠技术，但放射科医生的人工观察要求很高，且往往容易出现诊断错误。因此，计算机辅助诊断（CADx）系统可能是一种可行的替代方案，可以促进和简化癌症诊断过程；因此开展了本研究。
所提出模型的输入是从乳房X线图像分析协会数据库下载的原始乳房X线照片。在分类之前，对原始乳房X线照片进行预处理。然后，使用灰度共生矩阵从乳房X线照片中提取15个纹理特征，分别在四个不同的角度方向：θ={0°、45°、90°、135°}，以及两个距离：D={1,2}。之后，使用两阶段支持向量机将乳房X线照片分类为正常、良性和恶性。
用作测试数据的所有37张正常图像均被分类为正常（无假阳性），所有41张异常图像均被正确分类为异常（无假阴性），这意味着该模型检测异常的灵敏度和特异性均为100%。在检测到异常后，系统进一步将乳房X线照片上的异常分类为“良性”或“恶性”。在23张良性图像中，21张被正确分类为良性。此外，在18张恶性图像中，17张被正确分类为恶性。根据这些结果，该系统的灵敏度、特异性、阳性预测值和阴性预测值分别为94.4%、91.3%、89.5%和95.5%。
本文进一步证实了自动化CADx系统作为一种可行工具的能力，它可以帮助放射科医生进行乳腺癌诊断。
