Classification of asymmetry in mammography via the DenseNet convolutional neural network

PURPOSE: To investigate the effectiveness of a deep learning system based on the DenseNet convolutional neural network in diagnosing benign and malignant asymmetric lesions in mammography. METHODS: Clinical and image data from 460 women aged 23–82 years (47.57 ± 8.73 years) with asymmetric lesions who underwent mammography at Shenzhen People's Hospital, Shenzhen Luohu District People's Hospital, and Shenzhen Hospital of Peking University from December 2019 to December 2020 were retrospectively analyzed. Two senior radiologists, two junior radiologists, and the DL system read the mammographic images of 460 patients, respectively, and finally recorded the BI-RADS classification of asymmetric lesions. We then used the area under the curve (AUC) of the receiver operating characteristic (ROC) to evaluate the diagnostic efficacy and the difference between AUCs by the Delong method. RESULTS: Specificity (0.909 vs. 0.835, 0.790, [Formula: see text] =8.21 and 17.22, p＜0.05) and precision (0.872 vs. 0.763, 0.726, [Formula: see text] =9.23 and 5.22, p＜0.05) of the DL system in the diagnosis of benign and malignant asymmetric lesions were higher than those of junior radiologist A and B, and there was a statistically significant difference between AUCs (0.778 vs. 0.579, 0.564, Z = 4.033 and 4.460, p＜0.05). Furthermore, the AUC (0.778 vs. 0.904, 0.862, Z = 3.191, and 2.167, p＜0.05) of benign and malignant asymmetric lesions diagnosed by the DL system was lower than that of senior radiologist A and senior radiologist B. CONCLUSIONS: The DL system based on the DenseNet convolution neural network has high diagnostic efficiency, which can help junior radiologists evaluate benign and malignant asymmetric lesions more accurately. It can also improve diagnostic accuracy and reduce missed diagnoses caused by inexperienced junior radiologists.