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A Systematic Survey on Automatic Classification of Breast Cancer using Histopathology Image

Shwetha G.K
2020 Bioscience Biotechnology Research Communications  
Since, extracting non-redundant and informative features from histopathology image is a challenging task, due to heavy noise conditions, and small variant nuclei cell size.  ...  So, automatic classification of breast cancer utilizing image techniques has great application value in the early detection of breast cancer.  ...  (man et al. 2020) designed a deep learning architecture with handcrafted features for histopathology breast cancer detection.  ... 
doi:10.21786/bbrc/13.13/54 fatcat:fhcbqtgr4jd7zaqppebrrw24fq

Automated Detection and Classification of Breast Cancer Nuclei with Deep Convolutional Neural Network

Shanmugham Balasundaram, Revathi Balasundaram, Ganesan Rasuthevar, Christeena Joseph, Annie Grace Vimala, Nanmaran Rajendiran, Baskaran Kaliyamurthy
2021 Journal of ICT Research and Applications  
A ResNet-based convolutional neural network was adapted to perform end-to-end segmentation of breast cancer nuclei.  ...  This tissue morphology study was established through invasive ductal breast cancer histopathology images accessed from the Databiox public dataset.  ...  This segmentation preceded automatic detection and extraction of nuclei and lumen regions from H&Estained breast cancer histopathological images.  ... 
doi:10.5614/itbj.ict.res.appl.2021.15.2.3 doaj:1053e191b0214506b04e21e2844cb74d fatcat:vaiz26svenbn5l2q5tupzxh27m


Vijay M. Mane, Nikhil Tagalpallewar
2018 ICTACT Journal on Image and Video Processing  
In this paper a system for automatic detection of breast cancer grading of H&E stained histopathological images is presented.  ...  The grading of breast cancer histopathology images is used to find the level of breast cancer. The automatic grading of breast cancer histopathology images is a challenging task.  ...  This paper presents a method for finding the grade of breast cancer tissue images by Bloom Richardson grading system.  ... 
doi:10.21917/ijivp.2018.0257 fatcat:ldvfcgi37jbmxecaopd5ibc5oq

Nuclei extraction from histopathological images using a marked point process approach

Maria Kulikova, Antoine Veillard, Ludovic Roux, Daniel Racoceanu, David R. Haynor, Sébastien Ourselin
2012 Medical Imaging 2012: Image Processing  
The experiments are performed using a database of H&E stained breast cancer images covering a wide range of histological grades.  ...  The nuclei often appear joint or even overlap in histopathological images.  ...  Acknowledgments This work was performed within the project MICO COgnitive MIcroscope: a cognition-driven visual explorer for histopathology for application to breast cancer grading, a project supported  ... 
doi:10.1117/12.911757 dblp:conf/miip/KulikovaVRR12 fatcat:5vidb5osyfdbhdje2falthfg64

Cancerous Nuclei Detection and Scoring in Breast Cancer Histopathological Images [article]

Pegah Faridi, Habibollah Danyali, Mohammad Sadegh Helfroush, and Mojgan Akbarzadeh Jahromi
2016 arXiv   pre-print
This research is focused on detection and grading of nuclear pleomorphism in histopathological images of breast cancer. The proposed method consists of three internal steps.  ...  Early detection and prognosis of breast cancer are feasible by utilizing histopathological grading of biopsy specimens.  ...  A wide variety of CAD systems are offered for breast cancer detection and grading in histopathological images.  ... 
arXiv:1612.01237v1 fatcat:deibaebn2zae7h4umeu6btlsbi

Mitotic Cell Classification System Based On Supervised Learning for Histopathological Images of Breast Cancer

Considering these challenges, this work presents a mitotic cell classification system based on supervised learning for histopathological images of breast cancer.  ...  Breast cancer is a great threat to the women population throughout the world.  ...  In [11] , a Stacked Sparse Auto-Encoder (SSAE) for nuclei detection is presented for breast cancer histopathology images.  ... 
doi:10.35940/ijitee.k1552.0981119 fatcat:zyur3g6awbc2bhzg4fxct7xtvy

Detection of breast cancer on digital histopathology images: Present status and future possibilities

M.A. Aswathy, M. Jagannath
2017 Informatics in Medicine Unlocked  
This article reviews and summarizes the applications of digital image processing techniques on histopathological images for the detection of breast cancer and discusses its future possibilities.  ...  A B S T R A C T Breast cancer is a very common type of cancer in women around the world and more so in India. It affects not only women but also men.  ...  This article reviews different techniques used for histopathology image analysis with a focus on breast cancer detection and classification.  ... 
doi:10.1016/j.imu.2016.11.001 fatcat:f24ina3lejew5hv5tnn5kyspua

Histopathological Image Classification Scheme for Breast Tissues to Detect Mitosis

Understanding the benefits, this article presents a histopathological image classification scheme meant for breast tissues in detecting the mitotic cells.  ...  The quality of image interpretation can be improvised with the help of a computerized assisting system for image analysis.  ...  This work detects the mitosis on breast histopathology images by constructing a deep segmentation network for generating segmentation map.  ... 
doi:10.35940/ijitee.k1553.0981119 fatcat:x6oaurlfprh2zek6pn3kcd6g7m

Histopathological Image Analysis Using Image Processing Techniques: An Overview

A.D Belsare
2012 Signal & Image Processing An International Journal  
This paper reviews computer assisted histopathology image analysis for cancer detection and classification.  ...  To overcome this difficulty a computer assisted image analysis is needed for quantitative diagnosis of tissue.  ...  Medical College and Hospital, Nagpur, India for providing the medical image data and interpretation for the analysis.  ... 
doi:10.5121/sipij.2012.3403 fatcat:zeqdmbv7vbcptjeyv3uqfgfsru

An Automated Approach towards Detection of Mitosis in Histopathological Images

Anand Raj, T. N., Nandini Manoli
2018 International Journal of Computer Applications  
Generally, the grade of a breast cancer is considered as an "aggressive potential" in the growth of a tumor.  ...  In this research, an automated detection of mitosis from histopathological images is presented.  ...  Roullier et al [23] proposed a multi-resolution graph based method for automated detection of mitotic nuclei.  ... 
doi:10.5120/ijca2018916886 fatcat:t3vp32zilzbytjwowgpfe5cyd4

Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images

Jun Xu, Lei Xiang, Qingshan Liu, Hannah Gilmore, Jianzhong Wu, Jinghai Tang, Anant Madabhushi
2016 IEEE Transactions on Medical Imaging  
In this paper, a Stacked Sparse Autoencoder (SSAE), an instance of a deep learning strategy, is presented for efficient nuclei detection on high-resolution histopathological images of breast cancer.  ...  Automated nuclear detection is a critical step for a number of computer assisted pathology related image analysis algorithms such as for automated grading of breast cancer tissue specimens.  ...  Fig. 5 .Fig. 6 . 56 The nuclei detection results (b) of SSAE+SMC for a large breast histopathological image (a) from a whole-slide image of a patient.  ... 
doi:10.1109/tmi.2015.2458702 pmid:26208307 pmcid:PMC4729702 fatcat:xuz7swthcjanphpzhf7tqxcysu

Visualization for Histopathology Images using Graph Convolutional Neural Networks [article]

Mookund Sureka, Abhijeet Patil, Deepak Anand, Amit Sethi
2020 arXiv   pre-print
We adopt an approach to model histology tissue as a graph of nuclei and develop a graph convolutional network framework based on attention mechanism and node occlusion for disease diagnosis.  ...  The proposed method highlights the relative contribution of each cell nucleus in the whole-slide image.  ...  Fig. 2 . 2 Graph formation: We start with a histopathology image, detect all nuclei using a U-Net, and construct a graph by linking pairs of nuclei closer than a distance threshold.  ... 
arXiv:2006.09464v1 fatcat:wsl6hggfzveo3drhnu26yxmfyu

Deep neural network models for computational histopathology: A survey [article]

Chetan L. Srinidhi, Ozan Ciga, Anne L. Martel
2019 arXiv   pre-print
In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used in the context of histopathological image analysis.  ...  Recently, deep learning has become the mainstream methodological choice for analyzing and interpreting cancer histology images.  ...  In the context of FCN, the earlier methods by Chen et al. (2016a) ; Xie et al. (2018a) proposed a simple FCN based regression model for detecting cells in histopathology images.  ... 
arXiv:1912.12378v1 fatcat:xdfkzzwzb5alhjfhffqpcurb2u

A comparative evaluation of texture features for semantic segmentation of breast histopathological images

Rashmi R, Keerthana Prasad, Chethana Babu K. Udupa, Shwetha V
2020 IEEE Access  
Limited availability of breast histopathological image dataset with fine annotations for detection of nucleus has restricted the analysis of histopathological images at the pixel-level.  ...  Breast histopathological image analysis helps in understanding the structure and distribution of the nucleus, thereby assisting in the detection of breast cancer.  ...  [10] proposed a method to segment the nuclei from hepatocellular biopsy images.  ... 
doi:10.1109/access.2020.2984522 fatcat:slgjq7gbvzc4lc3igftryfwgum

Classification of Breast Cancer in Histopathology Image using Modified Ant Lion Optimizer and Capsule Network Architecture

Shwetha G.K
2020 Bioscience Biotechnology Research Communications  
In this exploration, a proper component choice and classification methods are proposed for programmed bosom malignancy discovery and characterization.  ...  The exact recognition of breast cancer disease utilizing histology pictures is a difficult assignment, because of the variety of generous tissue and heterogeneity of cell development.  ...  (Saha et al. 2018) developed a new supervised model for detecting mitosis from breast histopathological images.  ... 
doi:10.21786/bbrc/13.13/43 fatcat:rlmjtocudrc6zdw7bsr74eeake
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