Histopathological breast-image classification with image enhancement by convolutional neural network

Abdullah-Al Nahid; Ferdous Bin Ali; Yinan Kong

doi:10.1109/ICCITECHN.2017.8281815

Histopathological breast-image classification with image enhancement by convolutional neural network

Abdullah-Al Nahid, Ferdous Bin Ali, Yinan Kong

School of Engineering

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

17 Citations (Scopus)

Abstract

Finding malignancy from Histopathological images is always a challenging task. So far research has been carried out to classify Histopathological images using various techniques and methods. Recently, the state-of-the art Convolutional Neural Network (CNN) has largely been utilized for natural image classification. In this paper, using the advancement of CNN techniques, we have classified a set of Histopathological Breast images into Benign and Malignant classes, which can save doctors and physicians time and also allow patients a second opinion about the disease.

Original language	English
Title of host publication	20th International Conference of Computer and Information Technology
Subtitle of host publication	ICCIT 2017: December 22-24 UAP, Dhaka, Bangladesh
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781538611500, 9781538611494
ISBN (Print)	9781538611517
DOIs	https://doi.org/10.1109/ICCITECHN.2017.8281815
Publication status	Published - 2017
Event	20th International Conference of Computer and Information Technology, ICCIT 2017 - Dhaka, Bangladesh Duration: 22 Dec 2017 → 24 Dec 2017

Conference

Conference	20th International Conference of Computer and Information Technology, ICCIT 2017
Country/Territory	Bangladesh
City	Dhaka
Period	22/12/17 → 24/12/17

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10.1109/ICCITECHN.2017.8281815

Cite this

Nahid, A.-A., Ali, F. B., & Kong, Y. (2017). Histopathological breast-image classification with image enhancement by convolutional neural network. In 20th International Conference of Computer and Information Technology: ICCIT 2017: December 22-24 UAP, Dhaka, Bangladesh (pp. 1-6). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICCITECHN.2017.8281815

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title = "Histopathological breast-image classification with image enhancement by convolutional neural network",

abstract = "Finding malignancy from Histopathological images is always a challenging task. So far research has been carried out to classify Histopathological images using various techniques and methods. Recently, the state-of-the art Convolutional Neural Network (CNN) has largely been utilized for natural image classification. In this paper, using the advancement of CNN techniques, we have classified a set of Histopathological Breast images into Benign and Malignant classes, which can save doctors and physicians time and also allow patients a second opinion about the disease.",

author = "Abdullah-Al Nahid and Ali, {Ferdous Bin} and Yinan Kong",

year = "2017",

doi = "10.1109/ICCITECHN.2017.8281815",

language = "English",

isbn = "9781538611517",

pages = "1--6",

booktitle = "20th International Conference of Computer and Information Technology",

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

address = "United States",

note = "20th International Conference of Computer and Information Technology, ICCIT 2017 ; Conference date: 22-12-2017 Through 24-12-2017",

}

Nahid, A-A, Ali, FB & Kong, Y 2017, Histopathological breast-image classification with image enhancement by convolutional neural network. in 20th International Conference of Computer and Information Technology: ICCIT 2017: December 22-24 UAP, Dhaka, Bangladesh. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 1-6, 20th International Conference of Computer and Information Technology, ICCIT 2017, Dhaka, Bangladesh, 22/12/17. https://doi.org/10.1109/ICCITECHN.2017.8281815

Histopathological breast-image classification with image enhancement by convolutional neural network. / Nahid, Abdullah-Al; Ali, Ferdous Bin; Kong, Yinan.
20th International Conference of Computer and Information Technology: ICCIT 2017: December 22-24 UAP, Dhaka, Bangladesh. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 1-6.

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

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T1 - Histopathological breast-image classification with image enhancement by convolutional neural network

AU - Nahid, Abdullah-Al

AU - Ali, Ferdous Bin

AU - Kong, Yinan

PY - 2017

Y1 - 2017

N2 - Finding malignancy from Histopathological images is always a challenging task. So far research has been carried out to classify Histopathological images using various techniques and methods. Recently, the state-of-the art Convolutional Neural Network (CNN) has largely been utilized for natural image classification. In this paper, using the advancement of CNN techniques, we have classified a set of Histopathological Breast images into Benign and Malignant classes, which can save doctors and physicians time and also allow patients a second opinion about the disease.

AB - Finding malignancy from Histopathological images is always a challenging task. So far research has been carried out to classify Histopathological images using various techniques and methods. Recently, the state-of-the art Convolutional Neural Network (CNN) has largely been utilized for natural image classification. In this paper, using the advancement of CNN techniques, we have classified a set of Histopathological Breast images into Benign and Malignant classes, which can save doctors and physicians time and also allow patients a second opinion about the disease.

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SP - 1

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BT - 20th International Conference of Computer and Information Technology

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 20th International Conference of Computer and Information Technology, ICCIT 2017

Y2 - 22 December 2017 through 24 December 2017

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Nahid AA, Ali FB, Kong Y. Histopathological breast-image classification with image enhancement by convolutional neural network. In 20th International Conference of Computer and Information Technology: ICCIT 2017: December 22-24 UAP, Dhaka, Bangladesh. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2017. p. 1-6 doi: 10.1109/ICCITECHN.2017.8281815

Histopathological breast-image classification with image enhancement by convolutional neural network

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