scholarly journals Development of Breast Cancer Detection Model Using Transfer Learning of Residual Neural Network (ResNet-50)

2019 ◽  
Vol 1 (3) ◽  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Transfer Learning ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Detection Model

Backpropagation neural network for processing of missing data in breast cancer detection

IRBM ◽  
2021 ◽  
Author(s):  
L. Zhang ◽  
Hongyan Cui ◽  
Bingqing Liu ◽  
Chao Zhang ◽  
Berthold K.P. Horn
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Missing Data ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Backpropagation Neural Network

Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images

2020 ◽  
Vol 59 (17) ◽  
pp. E23 ◽  
Author(s):  
Esdras Chaves ◽  
Caroline B. Gonçalves ◽  
Marcelo K. Albertini ◽  
Soojeong Lee ◽  
Gwanggil Jeon ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Transfer Learning ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Infrared Images

scholarly journals Breast Cancer Detection using Residual Convolutional Neural Network and Weighted Loss

2019 ◽  
Vol 11 (2) ◽  
pp. 43
Author(s):  
Samuel Aji Sena ◽  
Panca Mudjirahardjo ◽  
Sholeh Hadi Pramono
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Ductal Carcinoma ◽  
Detection System ◽  
Breast Cancer Detection ◽  
University Of Pennsylvania ◽  
Training Approach

This research presents a breast cancer detection system using deep learning method. Breast cancer detection in a large slide of biopsy image is a hard task because it needs manual observation by a pathologist to find the malignant region. The deep learning model used in this research is made up of multiple layers of the residual convolutional neural network, and instead of using another type of classifier, a multilayer neural network was used as the classifier and stacked together and trained using end-to-end training approach. The system is trained using invasive ductal carcinoma dataset from the Hospital of the University of Pennsylvania and The Cancer Institute of New Jersey. From this dataset, 80% and 20% were randomly sampled and used as training and testing data respectively. Training a neural network on an imbalanced dataset is quite challenging. Weighted loss function was used as the objective function to tackle this problem. We achieve 78.26% and 78.03% for Recall and F1-Score metrics, respectively which are an improvement compared to the previous approach.

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