Deep Convolutional Neural Network-Based Analysis for Breast Cancer Histology Images

2020 ◽  
pp. 168-186
Author(s):  
E. Sudheer Kumar ◽  
C. Shoba Bindu ◽  
Sirivella Madhu
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Type ◽  
Network Architectures ◽  
Correct Treatment ◽  
In Situ Carcinoma ◽  
Tissue Organization ◽  
Histopathological Images ◽  
Aided Diagnosis

Breast cancer is one of the main causes of cancer death worldwide, and early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious. The relevance and potential of automatic classification algorithms using Hematoxylin-Eosin stained histopathological images have already been demonstrated, but the reported results are still sub-optimal for clinical use. Deep learning-based computer-aided diagnosis (CAD) has been gaining popularity for analyzing histopathological images. Based on the predominant cancer type, the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. The convolutional neural networks (CNN) is proposed to retrieve information at different scales, including both nuclei and overall tissue organization. This chapter utilizes several deep neural network architectures and gradient boosted trees classifier to classify the histology images among four classes. Hence, this approach has outperformed existing approaches in terms of accuracy and implementation complexity.

scholarly journals Assessment of Deep Learning Algorithms to Predict Histopathological Diagnosis of Breast Cancer: First Moroccan Prospective Study on a Private Dataset

2021 ◽  
Author(s):  
HAJAR EL AGOURI ◽  
Mohammed Azizi ◽  
Hicham El Attar ◽  
Mohammed El Khannoussi ◽  
Azeddine Ibrahimi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Sensitivity ◽  
Public Health Issue ◽  
Histopathological Diagnosis ◽  
Network Architectures ◽  
Deep Convolutional Neural Networks ◽  
In Situ Carcinoma ◽  
Histopathological Images ◽  
High Degree ◽  
Critical Public Health

Abstract Objective: Breast cancer is a critical public health issue and a leading cause of cancer-related deaths among women worldwide. Its early diagnosis and detection can effectively help in increasing the chances of survival rate. The aim of this work is to develop a computational approach based on deep convolutional neural networks for an efficient classification of breast cancer histopathological images by using our own created dataset. Two models of deep neural network architectures were used combined to gradient boosted trees classifier. Images were classified in three classes, normal tissue-benign lesions, in situ carcinoma and invasive carcinoma. Results: Both Resnet50 and Xception models achieved comparable results, with a small advantage to Xception extracted features. The proposed classification allowed us to obtain high degree of precision, a good generalization performance and avoided an eventual overfitting scenario due to the limited size of the data. In addition, we reported high sensitivity for detection of carcinoma cases, which is important for diagnostic pathology workflow in order to assist pathologists for diagnosing breast cancer with precision.

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.

Role of Ag NORs in Improve Diagnosis of Breast Cancer with Different grades and Subtypes’ among Sudanese women

2021 ◽  
Vol 1 (4) ◽  
pp. 443-448
Author(s):  
Doaa Ibrahim Ahmed
Keyword(s):  
Breast Cancer ◽  
Invasive Carcinoma ◽  
Ductal Carcinoma ◽  
Lobular Carcinoma ◽  
Age Group ◽  
Lobular Carcinoma In Situ ◽  
Tissue Sections ◽  
In Situ Carcinoma

This study aimed to evaluate the role of Ag NORs in improves diagnosis of Breast cancer with different subtypes’ among Sudanese Patients. This study include tissue sections of breast cancer diagnosed women, they were 30, ductal and lobular invasive carcinoma were 10 for each, while ductal and lobular in-situ carcinoma were 5 each. Found correlation between subtypes of breast cancer and Ag NOR , Invasive ductal carcinoma had more NOR while the lobular carcinoma in situ was less one , Stage III most frequency than the other stage. Silver staining were performed and Ag-NOR were detected in ductal and lobular invasive carcinoma more than ductal and lobular in-situ carcinoma, grade III has more frequency of Ag-NOR than other stages, and no correlation found between Ag-NOR and age group

Layer-Wise Tumor Segmentation of Breast Images Using Convolutional Neural Networks

2021 ◽  
pp. 70-84
Author(s):  
Nishanth Krishnaraj ◽  
A. Mary Mekala ◽  
Bhaskar M. ◽  
Ruban Nersisson ◽  
Alex Noel Joseph Raj
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Network Architecture ◽  
Breast Imaging ◽  
Breast Tissue ◽  
Imaging Techniques ◽  
Tumor Segmentation ◽  
Cancer Type ◽  
The Neural Network ◽  
Life Threatening

Early prediction of cancer type has become very crucial. Breast cancer is common to women and it leads to life threatening. Several imaging techniques have been suggested for timely detection and treatment of breast cancer. More research findings have been done to accurately detect the breast cancer. Automated whole breast ultrasound (AWBUS) is a new breast imaging technology that can render the entire breast anatomy in 3-D volume. The tissue layers in the breast are segmented and the type of lesion in the breast tissue can be identified which is essential for cancer detection. In this chapter, a u-net convolutional neural network architecture is used to implement the segmentation of breast tissues from AWBUS images into the different layers, that is, epidermis, subcutaneous, and muscular layer. The architecture was trained and tested with the AWBUS dataset images. The performance of the proposed scheme was based on accuracy, loss and the F1 score of the neural network that was calculated for each layer of the breast tissue.

scholarly journals A magyarországi emlő- és méhnyakszűrés retrospektív vizsgálatának jellemzői a halálozási és megbetegedési adatok tükrében

2019 ◽  
Vol 160 (49) ◽  
pp. 1948-1956
Author(s):  
Attila Sárváry ◽  
Pál Csaba Bálint ◽  
Anikó Gyulai ◽  
Zsigmond Kósa
Keyword(s):  
Breast Cancer ◽  
Cervical Cancer ◽  
Cancer Mortality ◽  
Breast Cancer Mortality ◽  
Mortality And Morbidity ◽  
Screening Programs ◽  
In Situ Carcinoma ◽  
Cervical Cancer Mortality ◽  
Morbidity Data

Abstract: Introduction: The organized breast and cervical screening programs were implemented in the framework of public health program in Hungary in order to reduce breast cancer mortality by 30% and cervical cancer mortality by 60% in given age groups within 10 years by 2012. Aim: The aim of our study was to conduct a retrospective analysis of mortality and morbidity data and to evaluate the effectiveness of the implemented screening programs. Method: Descriptive statistical analysis was performed by age-standardized mortality and morbidity data between 1980 and 2015 with special regard to the period of 2002–2012. Results: Breast cancer mortality of women aged 45–64 reduced by 28.3%, the incidence reduced by 23.6% and the incidence of in situ carcinoma increased by 242% between 2002 and 2012. Cervical cancer mortality of women aged 25–64 years reduced by 25.5%, the incidence reduced by 21.2%, and the incidence of in situ carcinoma increased by 13.3% during 2002–2012. Conclusion: Although both breast cancer and cervical cancer mortality substantially decreased in Hungary, the decrease in cervical cancer did not reach the target value. Orv Hetil. 2019; 160(49): 1948–1956.

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