Local and global feature utilization for breast image classification by convolutional neural network

Abdullah-Al Nahid, Yinan Kong

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

9 Citations (Scopus)

Abstract

Convolutional Neural Networks (CNN) have brought a revolutionary improvement to image analysis, especially in the image classification field. The technique of natural image classification using the CNN method has been deliberately utilized for medical image classification with some advanced engineering. However, so far in most of the cases CNN model classifies images based on global features extraction from the raw images. In this paper, we have utilized both raw images and some hand-crafted features, and later we classify images using a CNN network. For the classification purposes, we have utilized the BreakHis dataset and achieved a 96.00% accuracy, which is a state-of-the-art result on this dataset.

Original languageEnglish
Title of host publication2017 International Conference on Digital Image Computing
Subtitle of host publicationTechniques and Applications (DICTA)
EditorsYi Guo, Hongdong Li, Weidong (Tom) Cai, Manzur Murshed, Zhiyong Wang, Junbin Gao, David Dagan Feng
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages540-545
Number of pages6
ISBN (Electronic)9781538628393, 9781538628386
ISBN (Print)9781538628409
DOIs
Publication statusPublished - 2017
Event2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017 - Sydney, Australia
Duration: 29 Nov 20171 Dec 2017

Conference

Conference2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017
CountryAustralia
CitySydney
Period29/11/171/12/17

Fingerprint Dive into the research topics of 'Local and global feature utilization for breast image classification by convolutional neural network'. Together they form a unique fingerprint.

Cite this