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Performance of Novel Deep Learning Network with the Incorporation of the Automatic Segmentation Network for Diagnosis of Breast Cancer in Automated Breast Ultrasound by Qiucheng Wang; He Chen; Gongning Luo; Bo Li; Haitao Shang; Hua Shao; Shanshan Sun; Zhongshuai Wang; Kuanquan Wang; Wen Cheng is a Medicine article available to read on EtoBox.
What is Performance of Novel Deep Learning Network with the Incorporation of the Automatic Segmentation Network for Diagnosis of Breast Cancer in Automated Breast Ultrasound about?
Objective To develop novel deep learning network (DLN) with the incorporation of the automatic segmentation network (ASN) for morphological analysis and determined the performance for diagnosis breast cancer in automated breast ultrasound (ABUS). Methods A total of 769 breast tumors were enrolled in this study and were randomly divided into training set and test set at 600 vs. 169. The novel DLNs (Resent v2, ResNet50 v2, ResNet101 v2) added a new ASN to the traditional ResNet networks and extracted morphological information of breast tumors. The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic (ROC) curve (AUC), and average precision (AP) were calculated. The diagnostic performances of novel DLNs were compared with those of two radiologists with different experience. Results The ResNet34 v2 model had higher specificity (76.81%) and PPV (82.22%) than the other two, the ResNet50 v2 model had higher accuracy (78.11%) and NPV (72.86%), and the ResNet101 v2 model had higher sensitivity (85.00%). According to the AUCs and APs, the novel ResNet101 v2 model produced the best result (AUC 0.
Who reads Performance of Novel Deep Learning Network with the Incorporation of the Automatic Segmentation Network for Diagnosis of Breast Cancer in Automated Breast Ultrasound?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Qiucheng Wang; He Chen; Gongning Luo; Bo Li; Haitao Shang; Hua Shao; Shanshan Sun; Zhongshuai Wang; Kuanquan Wang; Wen Cheng
- Publisher
- Springer Science and Business Media LLC
- Published
- 2022
- Language
- EN
- Field
- Medicine (Health Sciences)