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.
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- 1.
Detailed Computational Graph of FS-Net is available at http://github.com/abhigoogol/FS-Net.
- 2.
Actual training time of FS-Net is 2.863 times faster than U-Net. Model is trained on Nvidia K80.
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Niyaz, U., Singh Sambyal, A., Padha, D. (2020). Evaluation of Deep Learning Model with Optimizing and Satisficing Metrics for Lung Segmentation. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_6
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