PURPOSE: We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound. MATERIALS AND METHODS: We retrospectively gathered 480 images of 96 benign masses and 467 images of 144 malignant masses for training data. Deep learning model was constructed using CNN architecture GoogLeNet and analyzed test data: 48 benign masses, 72 malignant masses. Three radiologists interpreted these test data. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. RESULTS: The CNN model and radiologists had a sensitivity of 0.958 and 0.583-0.917, specificity of 0.925 and 0.604-0.771, and accuracy of 0.925 and 0.658-0.792, respectively. The CNN model had equal or better diagnostic performance compared to radiologists (AUC = 0.913 and 0.728-0.845, p = 0.01-0.14). CONCLUSION: Deep learning with CNN shows high diagnostic performance to discriminate between benign and malignant breast masses on ultrasound.
PURPOSE: We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ultrasound. MATERIALS AND METHODS: We retrospectively gathered 480 images of 96 benign masses and 467 images of 144 malignant masses for training data. Deep learning model was constructed using CNN architecture GoogLeNet and analyzed test data: 48 benign masses, 72 malignant masses. Three radiologists interpreted these test data. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. RESULTS: The CNN model and radiologists had a sensitivity of 0.958 and 0.583-0.917, specificity of 0.925 and 0.604-0.771, and accuracy of 0.925 and 0.658-0.792, respectively. The CNN model had equal or better diagnostic performance compared to radiologists (AUC = 0.913 and 0.728-0.845, p = 0.01-0.14). CONCLUSION: Deep learning with CNN shows high diagnostic performance to discriminate between benign and malignant breast masses on ultrasound.
Keywords:
Artificial intelligence; Breast imaging; Convolutional neural network; Deep learning; Ultrasound