Predicting Invasive Ductal Carcinoma by Using Deep Convolutional Neural Network

DOI

10.2991/978-94-6463-040-4_29How to use a DOI?

Keywords

Invasive Ductal Carcinoma; Convolutional Neural Network; Breast Cancer

Abstract

Due to the nature of Breast Cancer, it is challenging to make correct diagnosis based on histopathology images. And it is crucial to make early diagnosis for a complete cure. In this paper, a Neural Network algorithm was proposed to train on sets of breast histopathology images. Based on Convolutional Neural Network (CNN), it can be realized to detect and extract spatial features of images. A deep Convolutional Neural Network architecture similar to VGGNet is proposed for this study, which contains 6 3 × 3 layers of depth-wise Convolutional layers, 3 pooling layers and 1 fully connected layer. The proposed model was trained using Kaggle dataset of breast histopathology images, 50 epochs, with batch size of 250. The model utilizes Adagrad optimizer with learning rate of 1 × 10–2, decay equal to value (i.e. learning rate/number of epochs), and Binary Crossentropy as loss function. The proposed model results in 91.28% accuracy and 0.22 loss.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shuaipeng Dong
PY  - 2022
DA  - 2022/12/27
TI  - Predicting Invasive Ductal Carcinoma by Using Deep Convolutional Neural Network
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 191
EP  - 196
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_29
DO  - 10.2991/978-94-6463-040-4_29
ID  - Dong2022
ER  -