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Evaluation of Deep Learning Model with Optimizing and Satisficing Metrics for Lung Segmentation

  • Usma NiyazEmail author
  • Abhishek Singh Sambyal
  • Devanand Padha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1048)

Abstract

The segmentation in medical image analysis is a crucial and prerequisite process during the diagnosis of the diseases. The need for segmentation is important to attain the region of interest where the probability of occurrence of an abnormality such as a nodule in the lungs or tumor in the brain is high. In this paper, we have proposed a new architecture called FS-Net which is a convolutional neural network- based model for the segmentation of lungs in CT scan images. It performs encoding of images into the feature maps and then decodes the feature maps into their respective lung masks. We have also trained the state-of-the-art U-Net on the same dataset and compared the results on the basis of optimizing and satisficing metrics. These metrics are useful for the selection of a better model with the maximum score at the satisfying condition. The FS-Net is computationally very efficient and achieves promising dice coefficient and loss score when compared with the U-Net taking one-third of the time.

Keywords

FS-Net U-Net Data augmentation Lung segmentation Neural network Deep learning 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Usma Niyaz
    • 1
    Email author
  • Abhishek Singh Sambyal
    • 1
  • Devanand Padha
    • 1
  1. 1.Central University of Jammu, J and KJammuIndia

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