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

Abdullah-Al Nahid, Ferdous Bin Ali, Yinan Kong

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 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 languageEnglish
Title of host publication20th International Conference of Computer and Information Technology
Subtitle of host publicationICCIT 2017: December 22-24 UAP, Dhaka, Bangladesh
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-6
Number of pages6
ISBN (Electronic)9781538611500, 9781538611494
ISBN (Print)9781538611517
DOIs
Publication statusPublished - 2017
Event20th International Conference of Computer and Information Technology, ICCIT 2017 - Dhaka, Bangladesh
Duration: 22 Dec 201724 Dec 2017

Conference

Conference20th International Conference of Computer and Information Technology, ICCIT 2017
CountryBangladesh
CityDhaka
Period22/12/1724/12/17

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  • 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). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICCITECHN.2017.8281815