3D Inverse Electromagnetic Solver Using Deep Neural Network Towards Breast Cancer Detection

2018 ◽  
Author(s):  
Veena Suresh ◽  
R. Kiran
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Detection ◽  
Deep Neural Network ◽  
Breast Cancer Detection ◽  
Electromagnetic Solver

scholarly journals Breast Cancer Detection using Deep Learning Techniques

2021 ◽  
Vol 9 (VI) ◽  
pp. 3959-3963
Author(s):  
Prof. M. S. Choudhari
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Survival Rate ◽  
Cancer Detection ◽  
Deep Neural Network ◽  
The United States ◽  
Breast Cancer Detection ◽  
Detection Techniques ◽  
Learning Techniques

Breast cancer is the most common form of cancer among women and the second most common cancer in the world (an estimated 1 152 161 new cases per year), trailing only lung cancer .The current approach to this disease involves early detection and treatment. This approach in the United States yields an 85% 10-year survival rate. Survival is directly related to stage at diagnosis, as can be seen by a 98% 10- year survival rate for patients with stages 0 and I disease compared with a 65% 10-year survival rate for patients with stage III disease. To improve survival in this disease, more patients need to be identified at an early stage.Therefore, we evaluated existing and emerging technologies used for breast cancer screening and detection to identify areas for potential improvement. The main criteria for a good screening test are accuracy, high sensitivity, ease of use, acceptability to the population being screened (with regard to discomfort and time), and low cost. We can begins by describing commonly used breast cancer detection techniques and then delves into emerging modalities. Several studies addressing breast cancer using Deep learning techniques. Many claim that their algorithms are faster, easier, or more accurate than others . This system is based on thermal image processing and Deep learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this was to optimize the learning algorithm. In this system , we applied the deep neural network technique to select the best features and perfect parameter values of the deep machine learning. The present study proves that deep neural network can automatically find the best model by combining feature preprocessing methods and classification algorithms.

Breast Cancer Detection using Deep Neural Network

2020 ◽  
Author(s):  
Md. Omaer Faruq Goni ◽  
Fahim Md. Sifnatul Hasnain ◽  
Md. Abu Ismail Siddique ◽  
Oishi Jyoti ◽  
Md. Habibur Rahaman
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Detection ◽  
Deep Neural Network ◽  
Breast Cancer Detection

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

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.

Sign in / Sign up

Export Citation Format

Share Document