Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach. (August 2017) - Xia, Jianfu; Chen, Huiling; Li, Qiang; Zhou, Minda; Chen, Limin; Cai, Zhennao; Fang, Yang; Zhou, Hong
Highlights: A new effective system based on extreme learning machine (ELM) is developed for detection of thyroid cancer. The proposed methodology is rigorously validated on the real-life data collected from Wenzhou Central Hospital. The performance of the system is augmented with smaller features chosen by ReliefF feature selection. The effectiveness of the proposed system has been evaluated by comparing with the widely used methods including SVM and BP neural networks. Abstract: Background and objectives: It is important to be able to accurately distinguish between benign and malignant thyroid nodules in order to make appropriate clinical decisions. The purpose of this study was to improve the effectiveness and efficiency for discriminating the malignant from benign thyroid cancers based on the Ultrasonography (US) features. Methods: There were 114 benign nodules in 106 patients (82 women and 24 men) and 89 malignant nodules in 81 patients (69 women and 12 men) included in this study. The potential of extreme learning machine (ELM) has been explored for the first time to discriminate malignant and benign thyroid nodules based on the sonographic features in ultrasound images. The influence of two key parameters (the number of hidden neurons and type of activation function) on the performance of ELM was investigated. The relationship between feature subsets obtained by the feature selection method and the classification performance of ELM was also examined. A real-life dataset was used to evaluate the effectiveness of the proposed method in terms of classification accuracy, sensitivity, specificity, and area under the ROC (receiver operating characteristic) curve (AUC). Results: The results demonstrate that there are significant differences between the malignant and benign thyroid nodules (p-value<0.01), the most discriminative features are echogenicity, calcification, margin, composition and shape. Compared with other methods, the proposed method not only has achieved very promising classification accuracy via 10-fold cross-validation (CV) scheme, but also greatly reduced the computational cost compared to other counterparts. The proposed ELM-based approach achieves 87.72% ACC, 0.8672 AUC, 78.89% sensitivity, and 94.55% specificity. Conclusions: Based on the empirical analysis, the proposed ELM-based approach for thyroid cancer detection has promising potential in clinical use, and it can be of assistance as an optional tool for the clinicians.