Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer.

Journal of Digital Imaging
Heqing ZhangJiangli Lin

Abstract

This study aimed to construct a breast ultrasound computer-aided prediction model based on the convolutional neural network (CNN) and investigate its diagnostic efficiency in breast cancer. A retrospective analysis was carried out, including 5000 breast ultrasound images (benign: 2500; malignant: 2500) as the training group. Different prediction models were constructed using CNN (based on InceptionV3, VGG16, ResNet50, and VGG19). Additionally, the constructed prediction models were tested using 1007 images of the test group (benign: 788; malignant: 219). The receiver operating characteristic curves were drawn, and the corresponding areas under the curve (AUCs) were obtained. The model with the highest AUC was selected, and its diagnostic accuracy was compared with that obtained by sonographers who performed and interpreted ultrasonographic examinations using 683 images of the comparison group (benign: 493; malignant: 190). In the model test with the test group images, the AUCs of the constructed InceptionV3, VGG16, ResNet50, and VGG19 models were 0.905, 0.866, 0.851, and 0.847, respectively. The InceptionV3 model showed the largest AUC, with statistically significant differences compared with the other models (P < 0.05). In the...Continue Reading

References

Jul 29, 2017·Physics in Medicine and Biology·Seokmin HanYeong-Kyeong Seong
Aug 3, 2018·Ultraschall in der Medizin : Organ der Deutschen Gesellschaft für Ultraschall in der Medizin, [der] Österreichischen Gesellschaft für Ultraschall in der Medizin, [der] Schweizerischen Gesellschaft für Ultraschall in Medizin und Biologie·Lea Marie PehrsonMichael Bachmann Nielsen
Oct 3, 2018·IEEE Transactions on Medical Imaging· Seung Yeon Shin Kyoung Mu Lee

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Citations

Oct 15, 2020·Neural Computing & Applications·Mehedi MasudM Shamim Hossain
Sep 15, 2020·Academic Radiology·Elana B SmithSheryl Jordan

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