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Exploring pretrained encoders for lung nodule segmentation task using LIDC-IDRI dataset

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Abstract

Deep learning has become ubiquitous in the field of computer vision for tasks such as image classification and segmentation. A Computer-Aided Diagnostic (CAD) system for lung cancer detection and diagnosis works by identifying lung nodules and characterizing the same. Transfer learning allows for pre-trained weights to be ported from one model to another. Replacement of pre-trained encoders in encoder-decoder networks opens up the number of possibilities of such networks and motivates us to check the possibility of each combination for a segmentation task of interest. This paper reports the experiments carried out using such combinations and presents the various observations as a result of the experiments for the nodule segmentation task on the LIDC-IDRI dataset. This work also examines the effect of network parameters on some of the deep learning semantic segmentation architectures in the context of the lung cancer dataset, LIDC-IDRI. The efficient network architecture, based on observations, is determined to be UNet with the backbone architecture, Efficientnet-b3 trained on the ImageNet dataset. This specific network presents an IoU score of 0.59 on the training dataset and 0.45 on the validation dataset. The architectures were compared and analyzed in terms of the time and space taken as well.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the LIDC-IDRI repository, https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI.

Code availability

The code for this work is available in https://github.com/sujijenkin/LIDC-IDRI-NoduleSeg_SEGMODELS.

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Funding

The authors would like to acknowledge the Kiran Division, Department of Science and Technology, Govt. of India, for funding this research work through the SR/WOSA/ET-153/2017 Research Grant. The authors also thank the anonymous reviewers for their encouraging reviews and recommendations.

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Authors and Affiliations

  1. ABV-IIITM, Gwalior, India

    R. Jenkin Suji, W. Wilfred Godfrey & Joydip Dhar

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  1. R. Jenkin Suji
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  2. W. Wilfred Godfrey
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  3. Joydip Dhar
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Correspondence to R. Jenkin Suji.

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Appendix: Graphs

Appendix: Graphs

Fig. 5
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Comparision of backbone performance (a Accuracy b Dice loss c F1-score d IoU score e Precision f Recall) of FPN base architecture

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Comparision of backbone performance (a Accuracy b Dice loss c F1-score d IoU score e Precision f Recall) of PSPNet base architecture

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Fig. 7
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Comparision of backbone performance (a Accuracy b Dice loss c F1-score d IoU score e Precision f Recall) of UNet base architecture

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Comparision of backbone performance (a Accuracy b Dice loss c F1-score d IoU score e Precision f Recall) of LinkNet base architecture

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Fig. 9
figure 9

Qualitatitive results of various architectures and backbones First row corresponds to various random 2D images and its groundtruth marked in red. The rest of the rows corresponds to FPN-efficientnet-b3, FPN-xception, Unet-efficientnet-b3, Unet-xception, PSPNet-InceptionV4, PSPNet-Densenet121, Linknet-Xception, Linknet-InceptionV4 respectively

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Suji, R.J., Godfrey, W.W. & Dhar, J. Exploring pretrained encoders for lung nodule segmentation task using LIDC-IDRI dataset. Multimed Tools Appl 83, 9685–9708 (2024). https://doi.org/10.1007/s11042-023-15871-3

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