Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8613
Title: Breast cancer histopathology image classification using CNN
Authors: Karatayev M.
Khalyk S.
Adai S.
Lee M.-H.
Demirci, Muhammed Fatih
Keywords: breast cancer
CancerNet
CNN
Deep Learning
IDC
Classification (of information)
Convolutional neural networks
Deep learning
Histology
Image classification
Medical imaging
Patient treatment
Tissue
Breast Cancer
Breast tissues
Cancernet
Convolutional neural network
Deep learning
Images classification
Invasive ductal carcinomata
Neural network model
Survival rate
Wide spreads
Diseases
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Karatayev, M., Khalyk, S., Adai, S., Lee, M. H., & Demirci, M. F. (2021, November). Breast cancer histopathology image classification using CNN. In 2021 16th International Conference on Electronics Computer and Computation (ICECCO) (pp. 1-5). IEEE.
Abstract: The breast cancer is one of the wide spread diseases around the world. Cancer develops in a milk duct and then spreads to the surrounding breast tissues. This initial stage of progression is called invasive ductal carcinomas (IDC). Almost 80% of all breast cancers are invasive ductal carcinomas. If IDC is detected at early stages, the patient can be treated and will have a high survival rate, whereas undetected cancer may spread into other parts of the body, as well as surrounding breast tissues. In this work, the dataset that contains breast cancer histopathology images was used. The objective of this work is to implement a convolutional neural network (CNN) model for accurate IDC classification, by balancing the dataset and tuning hyperparameters. The proposed model achieves an accuracy of 92% for the classification of histopathological images, and outperforms the baseline CancerNet model with accuracy of 86%. Furthermore, our experimental results demonstrate the superiority of our approach over the pre-Trained networks, such as VGG16, DenseNet and ResNet18. © 2021 IEEE.
Description: 16th International Conference on Electronics Computer and Computation, ICECCO 2021 -- 25 November 2021 through 26 November 2021 -- -- 176250
URI: https://doi.org/10.1109/ICECCO53203.2021.9663757
https://hdl.handle.net/20.500.11851/8613
ISBN: 9781665409452
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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