TITLE:
Detection and Classification of Lung Cancer Cells Using Swin Transformer
AUTHORS:
Yuru Chen, Jing Feng, Juan Liu, Baochuan Pang, Dehua Cao, Cheng Li
KEYWORDS:
Lung Cancer, Classification, Swin Transformer
JOURNAL NAME:
Journal of Cancer Therapy,
Vol.13 No.7,
July
21,
2022
ABSTRACT: Lung cancer is one of the greatest threats to human health. It is a very
effective way to detect lung cancer by pathological pictures of lung cancer
cells. Therefore, improving the accuracy and stability of diagnosis is very
important. In this study, we develop an automatic detection scheme for lung
cancer cells based on convolutional neural networks and Swin Transformer. Microscopic images of patients’ lung cells are first
segmented using a Mask R-CNN-based
network, resulting in a separate image for each cell. Part of the background
information is preserved by Gaussian blurring of surrounding cells, while the
target cells are highlighted. The classification model based on Swin
Transformer not only reduces the computation but also achieves better results
than the classical CNN model, ResNet50. The final results show that the
accuracy of the method proposed in this paper reaches 96.16%. Therefore, this
method is helpful for the detection and classification of lung cancer cells.