scholarly journals Modeling Deep Neural Networks For Breast Cancer Thermography Classification: A Review Study

2021 ◽  
Vol 13 (02) ◽  
pp. 4939-4946
Author(s):  
Amira Hassan Abed ◽  
Essam M Shaaban
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Review Study

Breast Cancer Detection From Thermography Based on Deep Neural Networks

2019 ◽  
Author(s):  
Seyfullah Kiymet ◽  
Muhammet Yavuz Aslankaya ◽  
Murat Taskiran ◽  
Bulent Bolat
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Cancer Detection ◽  
Deep Neural Networks ◽  
Breast Cancer Detection

Detection of Breast Cancer Using Infrared Thermography and Deep Neural Networks

2019 ◽  
pp. 514-523 ◽  
Author(s):  
Francisco Javier Fernández-Ovies ◽  
Edwin Santiago Alférez-Baquero ◽  
Enrique Juan de Andrés-Galiana ◽  
Ana Cernea ◽  
Zulima Fernández-Muñiz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Infrared Thermography ◽  
Deep Neural Networks

scholarly journals Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks

2018 ◽  
pp. 20170545 ◽  
Author(s):  
Jeremy R Burt ◽  
Neslisah Torosdagli ◽  
Naji Khosravan ◽  
Harish RaviPrakash ◽  
Aliasghar Mortazi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Recent Advances

scholarly journals OPTIMIZED DEEP NEURAL NETWORKS ARCHITECTURE MODEL FOR BREAST CANCER DIAGNOSIS

YMER Digital ◽  
2021 ◽  
Vol 20 (11) ◽  
pp. 161-175
Author(s):  
G Kanimozhi ◽  
◽  
P Shanmugavadivu ◽  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Cancer Detection ◽  
Deep Neural Networks ◽  
Medical Image Analysis ◽  
Optimization Techniques ◽  
Breast Cancer Detection ◽  
Automated System ◽  
Stochastic Gradient Descent ◽  
Architecture Model

Breast cancer has increasingly claimed the lives of women. Oncologists use digital mammograms as a viable source to detect breast cancer and classify it into benign and malignant based on the severity. The performance of the traditional methods on breast cancer detection could not be improved beyond a certain point due to the limitations and scope of computing. Moreover, the constrained scope of image processing techniques in developing automated breast cancer detection systems has motivated the researchers to shift their focus towards Artificial Intelligence based models. The Neural Networks (NN) have exhibited greater scope for the development of automated medical image analysis systems with the highest degree of accuracy. As NN model enables the automated system to understand the feature of problem-solving without being explicitly programmed. The optimization for NN offers an additional payoff on accuracy, computational complexity, and time. As the scope and suitability of optimization methods are data-dependent, the choice of selection of an appropriate optimization method itself is emerging as a prominent domain of research. In this paper, Deep Neural Networks (DNN) with different optimizers and Learning rates were designed for the prediction of breast cancer and its classification. Comparative performance analysis of five distinct first-order gradient-based optimization techniques, namely, Adaptive Gradient (Adagrad), Root Mean Square Propagation (RMSProp), Adaptive Delta (Adadelta), Adaptive Moment Estimation (Adam), and Stochastic Gradient Descent (SGD), is carried out to make predictions on the classification of breast cancer masses. For this purpose, the Mammographic Mass dataset was chosen for experimentation. The parameters determined for experiments were chosen on the number of hidden layers and learning rate along with hyperparameter tuning. The impacts of those optimizers were tested on the NN with One Hidden Layer (NN1HL), DNN with Three Hidden Layers (DNN4HL), and DNN with Eight Hidden Layers (DNN8HL). The experimental results showed that DNN8HL-Adam (DNN8HL-AM) had produced the highest accuracy of 91% among its counterparts. This research endorsed that the incorporation of optimizers in DNN contributes to an increased accuracy and optimized architecture for automated system development using neural networks.

scholarly journals A Systematic Approach to Identify the Breast Cancer Grades in Histopathological Images Using Deep Neural Networks

2021 ◽  
Author(s):  
S. H. S. Silva ◽  
Kasun Jinasena
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Systematic Approach ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Accuracy Rates

<div>The main intent of the research is to develop an automated application that can determine the Nottingham Histologic Score of a given input histopathological image obtained from breast cancer or healthy tissues with DenseNet based architecture. In this study, we were able to obtain more than 94% accuracy rates for each trained model including 2-predict, 3-predict, and 4-predict networks.</div>

Hazards of data leakage in machine learning: a study on classification of breast cancer using deep neural networks

2020 ◽  
Author(s):  
Ravi K. Samala ◽  
Heang-Ping Chan ◽  
Lubomir Hadjiiski ◽  
Sathvik Koneru
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks

scholarly journals Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening

2020 ◽  
Vol 39 (4) ◽  
pp. 1184-1194 ◽  
Author(s):  
Nan Wu ◽  
Jason Phang ◽  
Jungkyu Park ◽  
Yiqiu Shen ◽  
Zhe Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Cancer Screening ◽  
Breast Cancer Screening ◽  
Deep Neural Networks

Breast Cancer Histology Image Classification Based on Deep Neural Networks

2018 ◽  
pp. 827-836 ◽  
Author(s):  
Yao Guo ◽  
Huihui Dong ◽  
Fangzhou Song ◽  
Chuang Zhu ◽  
Jun Liu
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Image Classification ◽  
Deep Neural Networks
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