@inproceedings{5af0c3f5774445f5bfaed577234e255b,
title = "Optimization of CNN Model for Breast Cancer Classification",
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.",
keywords = "activation function, breast cancer, convolutional neural network, data balancing, deep learning",
author = "Nikolay Mikhailov and Mariam Shakeel and Abdybek Urmanov and Lee, {Min Ho} and Demirci, {M. Fatih}",
note = "Funding Information: V. ACKNOWLEDGEMENT This work was supported by Faculty Development Competitive Research Grant Program (No. 080420FD1909) at Nazarbayev University. Publisher Copyright: {\textcopyright} 2021 IEEE.; 16th International Conference on Electronics Computer and Computation, ICECCO 2021 ; Conference date: 25-11-2021 Through 26-11-2021",
year = "2021",
doi = "10.1109/ICECCO53203.2021.9663847",
language = "English",
series = "Proceedings - 2021 16th International Conference on Electronics Computer and Computation, ICECCO 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2021 16th International Conference on Electronics Computer and Computation, ICECCO 2021",
address = "United States",
}
