scholarly journals Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet)

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0232127 ◽  
Author(s):  
Xia Li ◽  
Xi Shen ◽  
Yongxia Zhou ◽  
Xiuhui Wang ◽  
Tie-Qiang Li
Keyword(s):  
Breast Cancer ◽  
Histopathological Images

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 .  

scholarly journals A Review of Breast Cancer Histopathological Image Classification

2021 ◽  
Vol 2129 (1) ◽  
pp. 012049
Author(s):  
Lei Huang ◽  
Azlan Mohd Zain ◽  
Kai-Qing Zhou ◽  
Chang-Feng Chen
Keyword(s):  
Breast Cancer ◽  
Image Classification ◽  
Histopathological Examination ◽  
Breast Cancer Diagnosis ◽  
Advantages And Disadvantages ◽  
Histopathological Image ◽  
Histopathological Images ◽  
Classification Of Images ◽  
Histopathological Image Classification

Abstract Breast Cancer (BC) is the most common malignant tumor for women in the world. Histopathological examination serves as basis for breast cancer diagnosis. Due to the low accuracy of histopathological images through manual judgment, the classification of histopathological images of breast cancer has become a research hotspot in the field of medical image processing. Accurate classification of images can help doctors to properly diagnoses and improve the survival rate of patients. This paper reviews the existing works on histopathological image classification of breast cancer and analysis the advantages and disadvantages of related algorithms. Findings of the histopathological image classification of the Breast Cancer study are drawn, and the possible future directions are also discussed.

scholarly journals Classification enhancement of breast cancer histopathological image using penalized logistic regression

2019 ◽  
Vol 13 (1) ◽  
pp. 405
Author(s):  
Mohammed Abdulrazaq Kahya
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Classification Accuracy ◽  
Area Under The Curve ◽  
Classification Performance ◽  
Histopathological Image ◽  
Classification Of Diseases ◽  
Histopathological Images ◽  
Penalized Logistic Regression

<p>Classification of breast cancer histopathological images plays a significant role in computer-aided diagnosis system. Features matrix was extracted in order to classify those images and they may contain outlier values adversely that affect the classification performance. Smoothing of features matrix has been proved to be an effective way to improve the classification result via eliminating of outlier values. In this paper, an adaptive penalized logistic regression is proposed, with the aim of smoothing features and provides high classification accuracy of histopathological images, by combining the penalized logistic regression with the smoothed features matrix. Experimental results based on a publicly recent breast cancer histopathological image datasets show that the proposed method significantly outperforms penalized logistic regression in terms of classification accuracy and area under the curve. Thus, the proposed method can be useful for histopathological images classification and other classification of diseases types using DNA gene expression data in the real clinical practice.</p>

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