scholarly journals Multi-Channel Local Binary Pattern Guided Convolutional Neural Network for Breast Cancer Classification

2021 ◽  
Vol 15 (1) ◽  
pp. 132-140
Author(s):  
Hiren Mewada ◽  
Jawad F. Al-Asad ◽  
Amit Patel ◽  
Jitendra Chaudhari ◽  
Keyur Mahant ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Local Binary Pattern ◽  
Texture Feature ◽  
Vital Role ◽  
Translation Invariant ◽  
Texture Information ◽  
Fixed Set ◽  
Histopathological Images

Background: The advancement in convolutional neural network (CNN) has reduced the burden of experts using the computer-aided diagnosis of human breast cancer. However, most CNN networks use spatial features only. The inherent texture structure present in histopathological images plays an important role in distinguishing malignant tissues. This paper proposes an alternate CNN network that integrates Local Binary Pattern (LBP) based texture information with CNN features. Methods: The study propagates that LBP provides the most robust rotation, and translation-invariant features in comparison with other texture feature extractors. Therefore, a formulation of LBP in context of convolution operation is presented and used in the proposed CNN network. A non-trainable fixed set binary convolutional filters representing LBP features are combined with trainable convolution filters to approximate the response of the convolution layer. A CNN architecture guided by LBP features is used to classify the histopathological images. Result: The network is trained using BreKHis datasets. The use of a fixed set of LBP filters reduces the burden of CNN by minimizing training parameters by a factor of 9. This makes it suitable for the environment with fewer resources. The proposed network obtained 96.46% of maximum accuracy with 98.51% AUC and 97% F1-score. Conclusion: LBP based texture information plays a vital role in cancer image classification. A multi-channel LBP futures fusion is used in the CNN network. The experiment results propagate that the new structure of LBP-guided CNN requires fewer training parameters preserving the capability of the CNN network’s classification accuracy.

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

scholarly journals Prediction of Breast Cancer images Classification Using Bidirectional Long Short Term Memory and Two-Dimensional Convolutional Neural network

2021 ◽  
Vol 9 (4) ◽  
pp. 29-38
Author(s):  
Oluwashola David Adeniji
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Screening Methods ◽  
Cancer Treatments ◽  
The Neural Network ◽  
Histopathological Images ◽  
Long Short Term Memory

Breast cancer is most prevalent among women around the world and Nigeria is no exception in this menace. The increased in survival rate is due to the dramatic advancement in the screening methods, early diagnosis, and discovery in cancer treatments. There is an improvement in different strategies of breast cancer classification. A model for   training   deep   neural networks   for classification   of   breast   cancer in histopathological images was developed in this study. However, this images are affected by data unbalance with the support of active learning. The output of the neural network on unlabeled samples was used to calculate weighted information entropy. It is utilized as uncertainty score for automatic selecting both samples with high and low confidence. A threshold   that   decays over iteration number is used   to   decide which high confidence samples should be concatenated with manually labeled samples and then used infine-tuning of convolutional neural network. The neural network was optionally trained using weighted cross-entropy loss to better cope with bias towards the majority class. The developed model was compared with the existing model. The accuracy level of 98.3% was achieved for the developed model while the existing model 93.97%. The accuracy gain of 4.33%. was achieved as performance in the prediction of breast cancer .  

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