Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8614
Title: Optimization of CNN Model for Breast Cancer Classification
Authors: Mikhailov N.
Shakeel M.
Urmanov A.
Lee M.-H.
Demirci M.F.
Keywords: activation function
breast cancer
convolutional neural network
data balancing
deep learning
Chemical activation
Convolution
Deep learning
Diseases
Medical imaging
Multilayer neural networks
Activation functions
Breast Cancer
Breast cancer classifications
Convolutional neural network
Data balancing
Deep learning
Learning techniques
Neural network model
Open-source
Optimisations
Convolutional neural networks
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Mikhailov, N., Shakeel, M., Urmanov, A., Lee, M. H., & Demirci, M. F. (2021, November). Optimization of CNN Model for Breast Cancer Classification. In 2021 16th International Conference on Electronics Computer and Computation (ICECCO) (pp. 1-3). IEEE.
Abstract: Application of deep learning techniques for breast cancer classification using histopathology images has gained interest during recent years. In this study, an open-source convolutional neural network (CNN) model developed for breast cancer classification model is optimized by performing sensitivities on various CNN parameters such as data balancing, activation functions and adding/removing CNN layers. Some of the parameters are less-sensitive in affecting model's performance. The results show that by balancing the number of positive and negative samples, accuracy of the model can be improved. However, some additional work is required to reach to that point. Furthermore, the computation time is reduced by almost 30% by increasing the learning rate from 0.01 to 0.05 while the training and validation accuracy and loss are comparable to that of the original CNN model. © 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/ICECCO53203.2021.9663847
https://hdl.handle.net/20.500.11851/8614
ISBN: 9781665409452
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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