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 |
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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 | |
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 |
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Country/Territory | Bangladesh |
City | Dhaka |
Period | 22/12/17 → 24/12/17 |