Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging
المؤلفون المشاركون
Chen, Zhi-Yi
Liang, Xiaowen
Yu, Jinsui
Liao, Jianyi
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-01-10
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Objective.
The incidence of superficial organ diseases has increased rapidly in recent years.
New methods such as computer-aided diagnosis (CAD) are widely used to improve diagnostic efficiency.
Convolutional neural networks (CNNs) are one of the most popular methods, and further improvements of CNNs should be considered.
This paper aims to develop a multiorgan CAD system based on CNNs for classifying both thyroid and breast nodules and investigate the impact of this system on the diagnostic efficiency of different preprocessing approaches.
Methods.
The training and validation sets comprised randomly selected thyroid and breast nodule images.
The data were subgrouped into 4 models according to the different preprocessing methods (depending on segmentation and the classification method).
A prospective data set was selected to verify the clinical value of the CNN model by comparison with ultrasound guidelines.
Diagnostic efficiency was assessed based on receiver operating characteristic (ROC) curves.
Results.
Among the 4 models, the CNN model using segmented images for classification achieved the best result.
For the validation set, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC) of our CNN model were 84.9%, 69.0%, 62.5%, 88.2%, 75.0%, and 0.769, respectively.
There was no statistically significant difference between the CNN model and the ultrasound guidelines.
The combination of the two methods achieved superior diagnostic efficiency compared with their use individually.
Conclusions.
The study demonstrates the probability, feasibility, and clinical value of CAD in the ultrasound diagnosis of multiple organs.
The use of segmented images and classification by the nature of the disease are the main factors responsible for the improvement of the CNN model.
Moreover, the combination of the CNN model and ultrasound guidelines results in better diagnostic performance, which will contribute to the improved diagnostic efficiency of CAD systems.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liang, Xiaowen& Yu, Jinsui& Liao, Jianyi& Chen, Zhi-Yi. 2020. Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging. BioMed Research International،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1131932
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liang, Xiaowen…[et al.]. Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging. BioMed Research International No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1131932
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liang, Xiaowen& Yu, Jinsui& Liao, Jianyi& Chen, Zhi-Yi. Convolutional Neural Network for Breast and Thyroid Nodules Diagnosis in Ultrasound Imaging. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1131932
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1131932
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر