A deep learning model integrating mammography and clinical factors facilitates the malignancy prediction of BI-RADS 4 microcalcifications in breast cancer screening

2021 ◽  
Author(s):  
Huanhuan Liu ◽  
Yanhong Chen ◽  
Yuzhen Zhang ◽  
Lijun Wang ◽  
Ran Luo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
Learning Model ◽  
Clinical Factors ◽  
Deep Learning Model

scholarly journals Deep learning model for fully automated breast cancer detection system from thermograms

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262349
Author(s):  
Esraa A. Mohamed ◽  
Essam A. Rashed ◽  
Tarek Gaber ◽  
Omar Karam
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Detection System ◽  
Diagnostic Technique ◽  
Learning Model ◽  
The Body ◽  
Breast Cancer Detection ◽  
Benchmark Database ◽  
Deep Learning Model

Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

scholarly journals Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Zhongyi Han ◽  
Benzheng Wei ◽  
Yuanjie Zheng ◽  
Yilong Yin ◽  
Kejian Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Histopathological Images ◽  
Multi Classification ◽  
Deep Learning Model

A high specificity deep learning approach with focus on breast cancer screening

2021 ◽  
Author(s):  
Pedro Vilares ◽  
Joao Ferreira ◽  
Luis Bastiao Silva ◽  
Augusto Silva
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
High Specificity ◽  
Learning Approach

scholarly journals Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening

2018 ◽  
Vol 24 (23) ◽  
pp. 5902-5909 ◽  
Author(s):  
Sarah S. Aboutalib ◽  
Aly A. Mohamed ◽  
Wendie A. Berg ◽  
Margarita L. Zuley ◽  
Jules H. Sumkin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Screening ◽  
Breast Cancer Screening

scholarly journals Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model

2021 ◽  
Vol 11 ◽  
Author(s):  
Xianyu Zhang ◽  
Hui Li ◽  
Chaoyun Wang ◽  
Wen Cheng ◽  
Yuntao Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Molecular Subtypes ◽  
Learning Model ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Test Set ◽  
External Test ◽  
Deep Learning Model

Background: Breast ultrasound is the first choice for breast tumor diagnosis in China, but the Breast Imaging Reporting and Data System (BI-RADS) categorization routinely used in the clinic often leads to unnecessary biopsy. Radiologists have no ability to predict molecular subtypes with important pathological information that can guide clinical treatment.Materials and Methods: This retrospective study collected breast ultrasound images from two hospitals and formed training, test and external test sets after strict selection, which included 2,822, 707, and 210 ultrasound images, respectively. An optimized deep learning model (DLM) was constructed with the training set, and the performance was verified in both the test set and the external test set. Diagnostic results were compared with the BI-RADS categorization determined by radiologists. We divided breast cancer into different molecular subtypes according to hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression. The ability to predict molecular subtypes using the DLM was confirmed in the test set.Results: In the test set, with pathological results as the gold standard, the accuracy, sensitivity and specificity were 85.6, 98.7, and 63.1%, respectively, according to the BI-RADS categorization. The same set achieved an accuracy, sensitivity, and specificity of 89.7, 91.3, and 86.9%, respectively, when using the DLM. For the test set, the area under the curve (AUC) was 0.96. For the external test set, the AUC was 0.90. The diagnostic accuracy was 92.86% with the DLM in BI-RADS 4a patients. Approximately 70.76% of the cases were judged as benign tumors. Unnecessary biopsy was theoretically reduced by 67.86%. However, the false negative rate was 10.4%. A good prediction effect was shown for the molecular subtypes of breast cancer with the DLM. The AUC were 0.864, 0.811, and 0.837 for the triple-negative subtype, HER2 (+) subtype and HR (+) subtype predictions, respectively.Conclusion: This study showed that the DLM was highly accurate in recognizing breast tumors from ultrasound images. Thus, the DLM can greatly reduce the incidence of unnecessary biopsy, especially for patients with BI-RADS 4a. In addition, the predictive ability of this model for molecular subtypes was satisfactory,which has specific clinical application value.

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