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

Abdullah-Al Nahid; Yinan Kong

doi:10.1109/DICTA.2017.8227460

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

Abdullah-Al Nahid, Yinan Kong

School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

13 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 language	English
Title of host publication	2017 International Conference on Digital Image Computing
Subtitle of host publication	Techniques and Applications (DICTA)
Editors	Yi Guo, Hongdong Li, Weidong (Tom) Cai, Manzur Murshed, Zhiyong Wang, Junbin Gao, David Dagan Feng
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	540-545
Number of pages	6
ISBN (Electronic)	9781538628393, 9781538628386
ISBN (Print)	9781538628409
DOIs	https://doi.org/10.1109/DICTA.2017.8227460
Publication status	Published - 2017
Event	2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017 - Sydney, Australia Duration: 29 Nov 2017 → 1 Dec 2017

Conference

Conference	2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017
Country/Territory	Australia
City	Sydney
Period	29/11/17 → 1/12/17

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10.1109/DICTA.2017.8227460

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Nahid, A.-A., & Kong, Y. (2017). Local and global feature utilization for breast image classification by convolutional neural network. In Y. Guo, H. Li, W. Cai, M. Murshed, Z. Wang, J. Gao, & D. D. Feng (Eds.), 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (pp. 540-545). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/DICTA.2017.8227460

Nahid, Abdullah-Al ; Kong, Yinan. / Local and global feature utilization for breast image classification by convolutional neural network. 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). editor / Yi Guo ; Hongdong Li ; Weidong (Tom) Cai ; Manzur Murshed ; Zhiyong Wang ; Junbin Gao ; David Dagan Feng. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2017. pp. 540-545

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title = "Local and global feature utilization for breast image classification by convolutional neural network",

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.",

author = "Abdullah-Al Nahid and Yinan Kong",

year = "2017",

doi = "10.1109/DICTA.2017.8227460",

language = "English",

isbn = "9781538628409",

pages = "540--545",

editor = "Yi Guo and Hongdong Li and Cai, {Weidong (Tom)} and Manzur Murshed and Zhiyong Wang and Junbin Gao and Feng, {David Dagan}",

booktitle = "2017 International Conference on Digital Image Computing",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017 ; Conference date: 29-11-2017 Through 01-12-2017",

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Nahid, A-A & Kong, Y 2017, Local and global feature utilization for breast image classification by convolutional neural network. in Y Guo, H Li, W Cai, M Murshed, Z Wang, J Gao & DD Feng (eds), 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 540-545, 2017 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2017, Sydney, Australia, 29/11/17. https://doi.org/10.1109/DICTA.2017.8227460

Local and global feature utilization for breast image classification by convolutional neural network. / Nahid, Abdullah-Al; Kong, Yinan.
2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). ed. / Yi Guo; Hongdong Li; Weidong (Tom) Cai; Manzur Murshed; Zhiyong Wang; Junbin Gao; David Dagan Feng. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 540-545.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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N2 - 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.

AB - 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.

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Nahid AA, Kong Y. Local and global feature utilization for breast image classification by convolutional neural network. In Guo Y, Li H, Cai W, Murshed M, Wang Z, Gao J, Feng DD, editors, 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2017. p. 540-545 doi: 10.1109/DICTA.2017.8227460

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

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