Breast cancer classification using a constructed convolutional neural network on the basis of the histopathological images by an interactive web-based interface

2019 ◽  
Author(s):  
Ahmet Kadir Arslan ◽  
Seyma Yasar ◽  
Cemil Colak
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Cancer Classification ◽  
Web Based ◽  
Breast Cancer Classification ◽  
Histopathological Images

scholarly journals Breast Cancer Classification Using Deep Convolutional Neural Networks

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Jennifer K Chukwu ◽  
Faisal B. Sani ◽  
Aliyu S. Nuhu
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Cancer Classification ◽  
Computer Assisted ◽  
Final Decision ◽  
Deep Convolutional Neural Networks ◽  
Breast Cancer Classification ◽  
Histopathological Images

Breast cancer remains the primary causes of death for women and much effort has been depleted in the form of screening series for prevention. Given the exponential growth in the number of mammograms collected, computer-assisted diagnosis has become a necessity. Histopathological imaging is one of the methods for cancer diagnosis where Pathologists examine tissue cells under different microscopic standards but disagree on the final decision. In this context, the use of automatic image processing techniques resulting from deep learning denotes a promising avenue for assisting in the diagnosis of breast cancer. In this paper, an android software for breast cancer classification using deep learning approach based on a Convolutional Neural Network (CNN) was developed. The software aims to classify the breast tumors to benign or malignant. Experimental results on histopathological images using the BreakHis dataset shows that the DenseNet CNN model achieved high processing performances with 96% of accuracy in the breast cancer classification task when compared with state-of-the-art modelsKeywords— Breast cancer classification, Convolutional Neural Network (CNN), deep learning, DenseNet, histopathological images  

scholarly journals Comparison Between Feature-Based and Convolutional Neural Network–Based Computer-Aided Diagnosis for Breast Cancer Classification in Digital Breast Tomosynthesis

2019 ◽  
pp. 1-18
Author(s):  
Siwa Chan ◽  
Jinn-Yi Yeh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Digital Breast Tomosynthesis ◽  
Cancer Classification ◽  
Breast Tomosynthesis ◽  
Breast Cancer Classification ◽  
Computer Aided ◽  
Feature Based ◽  
Aided Diagnosis

Digital breast tomosynthesis (DBT) is a promising new technique for breast cancer diagnosis. DBT has the potential to overcome the tissue superimposition problems that occur on traditional mammograms for tumor detection. However, DBT generates numerous images, thereby creating a heavy workload for radiologists. Therefore, constructing an automatic computer-aided diagnosis (CAD) system for DBT image analysis is necessary. This study compared feature-based CAD and convolutional neural network (CNN)-based CAD for breast cancer classification from DBT images. The research methods included image preprocessing, candidate tumor identification, three-dimensional feature generation, classification, image cropping, augmentation, CNN model design, and deep learning. The precision rates (standard deviation) of the LeNet-based CNN CAD and the feature-based CAD for breast cancer classification were 89.84 (0.013) and 84.46 (0.082), respectively. The T value was -4.091 and the P value was 0.00 < 0.05, which indicate that the LeNet-based CNN CAD significantly outperform the feature-based CAD. However, there is no significantly differences between the LeNet-based CNN CAD and the feature-based CAD on other criteria. The results can be applied to clinical medicine and assist radiologists in breast cancer identification.

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