scholarly journals Correction to “Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis”

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 214494-214494
Author(s):  
Jing Zheng ◽  
Denan Lin ◽  
Zhongjun Gao ◽  
Shuang Wang ◽  
Mingjie He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Adaboost Algorithm

scholarly journals Deep Learning Assisted Efficient AdaBoost Algorithm for Breast Cancer Detection and Early Diagnosis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96946-96954 ◽  
Author(s):  
Jing Zheng ◽  
Denan Lin ◽  
Zhongjun Gao ◽  
Shuang Wang ◽  
Mingjie He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Diagnosis ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Adaboost Algorithm

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.

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