Department of Computer & Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.
Department of Computer & Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan; PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan; Deep Learning Lab, Center for Mathematical Sciences (CMS), Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.
Comput Biol Med. 2021 May;132:104318. doi: 10.1016/j.compbiomed.2021.104318. Epub 2021 Mar 13.
Breast cancer is one of the deadly diseases among women. However, the chances of death are highly reduced if it gets diagnosed and treated at its early stage. Mammography is one of the reliable methods used by the radiologist to detect breast cancer at its initial stage. Therefore, an automatic and secure breast cancer detection system that accurately detects abnormalities not only increases the radiologist's diagnostic confidence but also provides more objective evidence. In this work, an automatic Diverse Features based Breast Cancer Detection (DFeBCD) system is proposed to classify a mammogram as normal or abnormal. Four sets of distinct feature types are used. Among them, features based on taxonomic indexes, statistical measures and local binary patterns are static. The proposed DFeBCD dynamically extracts the fourth set of features from mammogram images using a highway-network based deep convolution neural network (CNN). Two classifiers, Support Vector Machine (SVM) and Emotional Learning inspired Ensemble Classifier (ELiEC), are trained on these distinct features using a standard IRMA mammogram dataset. The reliability of the system performance is ensured by applying 5-folds cross-validation. Through experiments, we have observed that the performance of the DFeBCD system on dynamically generated features through highway network-based CNN is better than that of all the three individual sets of ad-hoc features. Furthermore, the hybridization of all four types of features improves the system's performance by nearly 2-3%. The performance of both the classifiers is comparable using the individual sets of ad-hoc features. However, the ELiEC classifier's performance is better than SVM using both hybrid and dynamic features.
乳腺癌是女性中最致命的疾病之一。然而，如果在早期发现并治疗，死亡的几率就会大大降低。乳腺 X 线摄影是放射科医生用于在早期阶段检测乳腺癌的一种可靠方法。因此，一个能够自动、安全地检测乳腺癌并准确检测异常的系统不仅可以提高放射科医生的诊断信心，还可以提供更客观的证据。在这项工作中，提出了一种基于多样特征的自动乳腺癌检测（DFeBCD）系统，用于将乳腺 X 线照片分类为正常或异常。使用了四组不同的特征类型。其中，基于分类索引、统计度量和局部二值模式的特征是静态的。所提出的 DFeBCD 使用基于高速公路网络的深度卷积神经网络（CNN）从乳腺 X 线图像中动态提取第四组特征。支持向量机（SVM）和情感学习启发式集成分类器（ELiEC）这两个分类器使用标准的 IRMA 乳腺 X 线数据集在这些不同的特征上进行训练。通过 5 折交叉验证来确保系统性能的可靠性。通过实验，我们观察到基于高速公路网络的 CNN 动态生成的特征的 DFeBCD 系统的性能优于所有三组特定特征。此外，所有四种类型的特征的混合可以将系统的性能提高近 2-3%。使用特定特征集时，两种分类器的性能相当。然而，使用混合和动态特征时，ELiEC 分类器的性能优于 SVM。
