Optimizing VGG16 for lung cancer CT image recognition: evaluating the effectiveness of channel and spatial attention

Guanzhen Li; Zongxia Li; Wenxu Shuai; Yue Wang

doi:10.1117/12.2628043

22 April 2022 Optimizing VGG16 for lung cancer CT image recognition: evaluating the effectiveness of channel and spatial attention

Guanzhen Li, Zongxia Li, Wenxu Shuai, Yue Wang

Author Affiliations +

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121634H (2022) https://doi.org/10.1117/12.2628043
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Deep learning is widely used today for training, classifying, and predicting large amounts of datasets today. One advantage of deep learning is that it can take many datasets as inputs to train a model. Then we can use the trained model to make predictions or do classifications quickly on many data that humans cannot even manage to do. The application of deep learning and computer vision has been used widely in the medical field, especially in the image diagnosis field. Indeed, this approach sometimes has done far better than human doctors. Although there is much research on classifying lung cancers, using deep learning, we still doubt on how different models perform on lung cancer image classification. Also, we are still concerned about how we can improve the accuracy rate of those models. In this paper, we will focus on image classification tasks and review the performance of the VGG16 model and identify whether adding attention layers to the model would make the model perform better. We will use lung cancer datasets collected from Lung Nodule Analysis 2016 to evaluate the model's performance.

Citation Download Citation

Guanzhen Li, Zongxia Li, Wenxu Shuai, and Yue Wang "Optimizing VGG16 for lung cancer CT image recognition: evaluating the effectiveness of channel and spatial attention", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121634H (22 April 2022); https://doi.org/10.1117/12.2628043

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