An automatic system for cell nuclei pleomorphism segmentation in histopathological images of breast cancer

2016 ◽  
Author(s):  
Pegah Faridi ◽  
Habibollah Danyali ◽  
Mohammad Sadegh Helfroush ◽  
Mojgan Akbarzadeh Jahromi
Keyword(s):  
Breast Cancer ◽  
Automatic System ◽  
Cell Nuclei ◽  
Histopathological Images

scholarly journals Upgraded Segmentation of Histopathological Images for Classification of Intraductal Breast Lesions

2019 ◽  
Vol 8 (2) ◽  
pp. 335-338
Keyword(s):  
Breast Cancer ◽  
Clustering Algorithm ◽  
Disease Classification ◽  
Research Subjects ◽  
Cell Nuclei ◽  
Segmentation Algorithms ◽  
Histopathological Images ◽  
Prime Objective ◽  
Inner Region

Cancer among women and the second most common cancer in the world is Breast Cancer(BC). This type of cancer-initiating from breast tissue, mostly from the inner region of milk ducts. The current progress in high-throughput and getting of digitized histological studies have made it possible to use histological pattern with image analysis to facilitate disease classification using computer-aided technology (CAT). The practice of analysis has become a part of the routine clinical discovery of breast cancer. In fact, CAT has become recent research subjects in the diagnostic of medical imaging and radiology. The vast increase in the capability of image acquisition and computational power in recent decades has prompted the development of several image segmentation algorithms. For the analysis of histopathological images, the automatic dissection of cell nuclei is an important stage. Its prime objective is to determine the exact location of the nuclei and boundary points of the cells. To accurately model the preference for histological structures (ducts, vessels, tumor nets, adipose, etc.). In the proposed method, additional k means clustering algorithm used for evaluating segmentation algorithms. Here demonstrate the in proposed methods over the state-of-the-art system in performance measures.

An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set

2020 ◽  
Vol 16 ◽  
Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Rashid ◽  
Jamal Hussain Shah ◽  
Muhammad Sharif ◽  
Muhammad Yahiya Haider Awan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Classification Performance ◽  
Diagnose Breast Cancer ◽  
Histopathological Images ◽  
And Performance ◽  
Learned Features ◽  
Intelligent Healthcare

Background: Breast cancer is considered as the most perilous sickness among females worldwide and the ratio of new cases is expanding yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. Objective: Most of these systems have either used traditional handcrafted features or deep features which had a lot of noise and redundancy, which ultimately decrease the performance of the system. Methods: A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pretrained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of proposed method. Results: The method concentrates on histopathological images to classify the breast cancer. The performance is compared with state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. Conclusion: The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.

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 Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Zhongyi Han ◽  
Benzheng Wei ◽  
Yuanjie Zheng ◽  
Yilong Yin ◽  
Kejian Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Histopathological Images ◽  
Multi Classification ◽  
Deep Learning Model
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