DenseNet Convolutional Neural Network for Breast Cancer Diagnosis

Xinkai Yuan; Lanrui Zhang; Shuming Zhao

doi:10.2991/978-94-6463-040-4_30

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DenseNet Convolutional Neural Network for Breast Cancer Diagnosis

Authors

Xinkai Yuan¹^{, *}, Lanrui Zhang²^{, *}, Shuming Zhao³^{, *}

¹Beijing JiaoTong University, Shang Yuan Street, Beijing, China

²School of Mathematics, University of Birmingham, Birmingham, B15 2TT, UK

³Sierra Canyon School, 20801 Rinaldi Street, Chatsworth, USA

^*Corresponding author. Email: 20723035@bjtu.edu.cn

^*Corresponding author. Email: lxz984@student.bham.ac.uk

^*Corresponding author. Email: Shuming.zhao@scsstudent.org

Corresponding Authors

Xinkai Yuan, Lanrui Zhang, Shuming Zhao

Available Online 27 December 2022.

DOI: 10.2991/978-94-6463-040-4_30 How to use a DOI?
Keywords: Breast cancer detection; Convolutional neural network; DenseNet
Abstract: Breast cancer is a fatal disease, among which, its sub-type invasive (or infiltrating) ductal carcinomas (IDC) dominate the death cases of it. Detecting the features of such disease in an X-ray image by human eyes can be challenged, especially in cancer’s early stage. Thus, this study is aimed at developing a system to assist doctors’ diagnoses and help the patient to have a preliminary understanding of their own health conditions. More specifically, an IDC detection system based on the Convolutional Neural Network (CNN) is developed, where the DenseNet121 is applied here. In fact, DenseNet 169 and DenseNet 201 are also tested but their performances are not as good as DenseNet121 in this study. As is expected, the system can automatically judge whether the region in a breast histology image is IDC positive or not. This method achieves a high precision, 0.9725 validation accuracy, 0.97 test accuracy, 0.96 recall, 0.96 F1-score, and 0.965 AUC in the sub-dataset selected from Kaggle’s Breast Histopathology images dataset. The time to predict 200 images is about 54 s and so the average prediction time for a single image is 2.7 s, which is fast enough for practical use.
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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Volume Title: Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
Series: Atlantis Highlights in Computer Sciences
Publication Date: 27 December 2022
ISBN: 10.2991/978-94-6463-040-4_30
ISSN: 2589-4900
DOI: 10.2991/978-94-6463-040-4_30 How to use a DOI?
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.

Cite this article

ris enw bib

TY  - CONF
AU  - Xinkai Yuan
AU  - Lanrui Zhang
AU  - Shuming Zhao
PY  - 2022
DA  - 2022/12/27
TI  - DenseNet Convolutional Neural Network for Breast Cancer Diagnosis
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 197
EP  - 202
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_30
DO  - 10.2991/978-94-6463-040-4_30
ID  - Yuan2022
ER  -

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