Home navigate_next Journals navigate_next ITIPPR navigate_next Vol. 4, No. 2navigate_next Breast Histopathological Image Feature Extraction with Convolutional Neural Networks for Classification
ICSES Transactions on Image Processing and Pattern Recognition
Vol. 4, No. 2, Jun. 2018

Breast Histopathological Image Feature Extraction with Convolutional Neural Networks for Classification

✔  Editors' Choice 

Original Paper
3.9k
Visits
661
Downloads
a Selcuk University, Konya, Turkey

 

Highlights and Novelties
1- Experiments done with permission from a new dataset presented in the global grand challenges 2018 ICIAR BACH histology microscopy image dataset.

2- Training Classical machine learning algorithms using automatically extracted features from medical images.

3- Automatic and faster breast cancer diagnosis using computer aided tools to help pathologist in breast cancer recognition and classification.

 

Manuscript Abstract
Recently, Convolutional Neural Networks (CNNs) have become a preferred deep learning artificial neural network of choice for computer assisted medical image analysis. These models are structured as a series of multiple hierarchical processing layers that can automatically learn feature representations from raw images. CNNs have in the past not been in common use, especially in the medical imaging field, due to issues such as insufficient image datasets. The revolution in CNN models has been attributed to powerful parallel processing hardware architectures, increasing number of image datasets and improved training strategies. Utilizing these deep learning techniques are enabling medical experts such as pathologists to utilize artificial intelligence to transform the world of medicine for faster and more accurate diagnoses. In this paper, a two stage model for classifying breast histopathological images is proposed. In the first stage, a CNN is used for extracting features from the images through a feature learning process. The extracted features are then used in the second stage to training classical machine learning models that include the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Logistic Regression (LR) models. The SVM classifier performs best with accuracies of up to 99.84%.

 

Keywords
 Biomedical Imaging   Convolutional Neural Network   Deep Learning   Histopathological Images   Support Vector Machine. 

 

Copyright and Licence
Copyright © International Computer Science and Engineering Society (ICSES). This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution Non Commercial 4.0 International (CC BY-NC 4.0) license, supported by creativecommons.orgcall_made

 

Cite this manuscript as
Kevin Kiambe, "Breast Histopathological Image Feature Extraction with Convolutional Neural Networks for Classification ," ICSES Transactions on Image Processing and Pattern Recognition, vol. 4, no. 2, pp. 4-12, Jun. 2018.

 

For External Scientific Databeses
--BibTex-- --EndNote-- --Dublin--
star The old version of this page can be accessed via here, and is supported till 2020.
Purchase and Access

lock_open Open-Access

Bibliography

Manuscript ID: 137
Pages: 4-12
Submitted: 2018-06-25
Revised: 2018-06-26
Accepted: 2018-06-27
Published: 2018-06-30

Cited By (0)
Journal's Title
ITIPPR Cover Page

Journal

ICSES Transactions on Image Processing and Pattern Recognition
ISSN: 2645-8071
ISSN: 2645-8071
Frequency: Quarterly
Accessability: Online - Open Access
Founded in: Mar. 2015
Publisher: ICSES
DOI Suffix: 10.31424/icses.itippr
close

Advertisement