Comparison of Cutting Edge Convolutional Neural Network for Breast Cancer Histopathology Image Diagnosis

DOI

10.2991/978-94-6463-040-4_101How to use a DOI?

Keywords

Breast cancer detection; Convolutional Neural Network; ResNet50V2; Inception V3; VGG 16

Abstract

Female breast cancer, especially the invasive ductal carcinoma, is a very common type of cancer. When breast cancer is diagnosed and treated at an early stage, patients can achieve a higher survival rate. In recent years, neural networks have shown their potential in medical fields. Therefore, this work focused on applying three state-of-the-art convolutional neural networks (CNNs), namely ResNet50V2, InceptionV3 and VGG16 to diagnosing breast cancer from histopathology images to verify whether CNNs can be an effective tool in this case. The three architectures were trained with an original dataset of breast cancer histopathology images. After the image pre-processing and the hyperparameter tuning, the evaluation and comparison of the networks’ performance were performed. They were evaluated through several statistical analysis based on accuracy, recall, precision, F1-score and training time. The experimental results showed that InceptionV3 obtained the best performance with the accuracy of 87.12% and F1-score of 86.99. ResNet50V2 achieved a close performance with a 74% training time compared with InceptionV3. The result proved that the state-of-the-art CNNs can be considered as a supportive tool that can help diagnosing breast cancers.

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.

Download article (PDF)

TY  - CONF
AU  - Tongyuan Qian
PY  - 2022
DA  - 2022/12/27
TI  - Comparison of Cutting Edge Convolutional Neural Network for Breast Cancer Histopathology Image Diagnosis
BT  - Proceedings of the 2022 3rd International Conference on Artificial Intelligence and Education (IC-ICAIE 2022)
PB  - Atlantis Press
SP  - 664
EP  - 669
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-040-4_101
DO  - 10.2991/978-94-6463-040-4_101
ID  - Qian2022
ER  -