Computer‐aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks. (July 2020)

• Record Type: Journal Article
• Authors: Moon, Woo Kyung; Lee, Yan-Wei; Ke, Hao-Hsiang; Lee, Su Hyun; Huang, Chiun-Sheng; Chang, Ruey-Feng
• Is Part Of: Computer methods and programs in biomedicine. Volume 190(2020)
• Publication Date: 2020-07
• Subjects: Breast cancer -- Breast ultrasound -- Computer-aided diagnosis -- Deep learning -- Convolutional neural network -- Ensemble learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105361
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A computer-aided diagnosis (CAD) system was proposed to diagnose breast cancer in ultrasound images. In this study, we propose a CAD system for tumor diagnosis using an image fusion method combined with different image content representations and ensemble different CNN architectures on US images. The results of our CAD system in the SNUH dataset show that the accuracy, sensitivity, specificity, precision, F1 score, and the AUC of the proposed method were 91.10%, 85.14%, 95.77%, 94.03%, 89.36%, and 0.9697, respectively. The results of our CAD system in the open dataset (BUSI) show that the accuracy, sensitivity, specificity, precision, F1 score, and the AUC of the proposed method were 94.62%, 92.31%, 95.60%, 90%, 91.14%, and 0.9711, respectively. Abstract: Breast ultrasound and computer aided diagnosis (CAD) has been used to classify tumors into benignancy or malignancy. However, conventional CAD software has some problems (such as handcrafted features are hard to design; conventional CAD systems are difficult to confirm overfitting problems, etc.). In our study, we propose a CAD system for tumor diagnosis using an image fusion method combined with different image content representations and ensemble different CNN architectures on US images. The CNN-based method proposed in this study includes VGGNet, ResNet, and DenseNet. In our private dataset, there was a total of 1687 tumors that including 953 benign and 734 malignant tumors. The accuracy, sensitivity, specificity, precision, F1 score and the AUC of the proposed method were 91.10%, 85.14%, 95.77%, 94.03%, 89.36%, and 0.9697 respectively. In the open dataset (BUSI), there was a total of 697 tumors that including 437 benign lesions, 210 malignant tumors, and 133 normal images. The accuracy, sensitivity, specificity, precision, F1 score, and the AUC of the proposed method were 94.62%, 92.31%, 95.60%, 90%, 91.14%, and 0.9711. In conclusion, the results indicated different image content representations that affect the prediction performance of the CAD system, more image information improves the prediction performance, and the tumor shape feature can improve the diagnostic effect.