Research Article
Binary Classification for Lung Nodule Based on Channel Attention Mechanism
@INPROCEEDINGS{10.1007/978-3-030-93179-7_16, author={Khai Dinh Lai and Thai Hoang Le and Thuy Thanh Nguyen}, title={Binary Classification for Lung Nodule Based on Channel Attention Mechanism}, proceedings={Context-Aware Systems and Applications. 10th EAI International Conference, ICCASA 2021, Virtual Event, October 28--29, 2021, Proceedings}, proceedings_a={ICCASA}, year={2022}, month={1}, keywords={Attention convolutional network Nodules detection SE block}, doi={10.1007/978-3-030-93179-7_16} }- Khai Dinh Lai
Thai Hoang Le
Thuy Thanh Nguyen
Year: 2022
Binary Classification for Lung Nodule Based on Channel Attention Mechanism
ICCASA
Springer
DOI: 10.1007/978-3-030-93179-7_16
Abstract
In order to effectively handle the problem of tumor detection on the LUNA16 dataset, we present a new methodology for data augmentation to address the issue of imbalance between the number of positive and negative candidates in this study. Furthermore, a new deep learning model - ASS (a model that combines Convnet sub-attention with Softmax loss) is also proposed and evaluated on patches with different sizes of the LUNA16. Data enrichment techniques are implemented in two ways: off-line augmentation increases the number of images based on the image under consideration, and on-line augmentation increases the number of images by rotating the image at four angles (0°, 90°, 180°, and 270°). We build candidate boxes of various sizes based on the coordinates of each candidate, and these candidate boxes are used to demonstrate the usefulness of the suggested ASS model. The results of cross-testing (with four cases: case 1, ASS trained and tested on a dataset of size 50 × 50; case 2, using ASS trained on a dataset of size 50 × 50 to test a dataset of size 100 × 100; case 3, ASS trained and tested on a dataset of size 100 × 100 and case 4, using ASS trained on a dataset of size 100 × 100 to test a dataset of size 50 × 50) show that the proposed ASS model is feasible.
