Classification of breast masses on ultrasound shear wave elastography using convolutional neural networks.

Classification of breast masses on ultrasound shear wave elastography using convolutional neural networks. Ultrason Imaging. 2020 Jun 05;:161734620932609 Authors: Fujioka T, Katsuta L, Kubota K, Mori M, Kikuchi Y, Kato A, Oda G, Nakagawa T, Kitazume Y, Tateishi U Abstract We aimed to use deep learning with convolutional neural networks (CNNs) to discriminate images of benign and malignant breast masses on ultrasound shear wave elastography (SWE). We retrospectively gathered 158 images of benign masses and 146 images of malignant masses as training data for SWE. A deep learning model was constructed using several CNN architectures (Xception, InceptionV3, InceptionResNetV2, DenseNet121, DenseNet169, and NASNetMobile) with 50, 100, and 200 epochs. We analyzed SWE images of 38 benign masses and 35 malignant masses as test data. Two radiologists interpreted these test data through a consensus reading using a 5-point visual color assessment (SWEc) and the mean elasticity value (in kPa) (SWEe). Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. The best CNN model (which was DenseNet169 with 100 epochs), SWEc, and SWEe had a sensitivity of 0.857, 0.829, and 0.914 and a specificity of 0.789, 0.737, and 0.763 respectively. The CNNs exhibited a mean AUC of 0.870 (range, 0.844-0.898), and SWEc and SWEe had an AUC of 0.821 and 0.855. The CNNs had an equal or better diagnostic performance co...
Source: Ultrasonic Imaging - Category: Radiology Tags: Ultrason Imaging Source Type: research