Lymph node sections detection based on deep convolutional neural networks

Yuchen Song; Xuejian Zhang

doi:10.1117/12.2622998

1 December 2021 Lymph node sections detection based on deep convolutional neural networks

Yuchen Song, Xuejian Zhang

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120792F (2021) https://doi.org/10.1117/12.2622998
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

Due to the severe damage to the health of a human being caused by breast cancer, it is rather crucial to explore novel approaches to detect images of lymph node sections to replace traditional methods, which are both time-consuming and inaccurate. This paper proposed an efficient cancer detection method by applying various kinds of convolutional neural networks (CNN) with a user interface. To achieve the best accuracy, experiments and comparisons are made between VGG16, ResNet-18, and EfficientNet, which are three popular models in the field of image processing. We also made a comparison to emphasize the importance of pre-trained weights of VGG16. Our models achieved high accuracy with pre-trained weights. In particular, VGG16 achieved 0.8340 in accuracy and 0.8326 in recall on the PatchCamelyon dataset and was used in our application. Our source code is available at https://github.com/bqdqj/Cancer-detection-based-on-tensorflow-and-PyQt5.

Citation Download Citation

Yuchen Song and Xuejian Zhang "Lymph node sections detection based on deep convolutional neural networks", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120792F (1 December 2021); https://doi.org/10.1117/12.2622998

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