Automated U-Net-ConvMixer Attention method for Lung Segmentation

  • Farah Oubelkas Hassan First University of Settat
  • Lahcen Moumoun Hassan First University of Settat
  • Abdellah Jamali Hassan First University of Settat

Abstract

Accurate lung segmentation in chest X-rays is vital for diagnosing various pulmonary pathologies. While U-Net architectures and their derivatives have achieved success in medical applications, their local convolution operations inherently limit their ability to capture global contextual information. In this work, we present a novel ConvMixer-based model for lung segmentation. Inspired by the ConvMixer architecture, this model effectively extracts both local and global features from lung images. To improve our segmentation results, we proposed a post-processing step in order to eliminate weakly contributing features from the segmentation. We evaluate our model on two publicly available chest X-ray datasets, Shenzhen and Montgomery, demonstrating superior performance compared to state-of-the-art segmentation methods. Notably, our final model achieves an accuracy of 97.52% and an IoU of 92.71%. These results suggest the proposed ConvMixer-based model as a promising approach for lung segmentation with the potential to contribute to improved diagnosis of various lung diseases.
Published
Sep 12, 2024
How to Cite
OUBELKAS, Farah; MOUMOUN, Lahcen; JAMALI, Abdellah. Automated U-Net-ConvMixer Attention method for Lung Segmentation. International Journal of Information Science and Technology, [S.l.], v. 8, n. 3, p. 28 - 33, sep. 2024. ISSN 2550-5114. Available at: <https://innove.org/ijist/index.php/ijist/article/view/260>. Date accessed: 14 oct. 2024. doi: http://dx.doi.org/10.57675/IMIST.PRSM/ijist-v8i3.260.
Section
Articles