Abstract
Lung field segmentation is the first step towards the development of any computer aided diagnosis (CAD) system for interstitial lung diseases (ILD) observed in chest high resolution computed tomography (HRCT) images. If the segmentation is not done efficiently it will compromise the accuracy of CAD system. In this paper, a deep learning-based method is proposed to localize several interstitial lung disease patterns (ILD) in HRCT images without performing lung field segmentation. In this paper, localization of several ILD patterns is performed in image slice. The pretrained models of ZF and VGG networks were fine-tuned in order to localize ILD patterns using Faster R-CNN framework. The three most difficult ILD patterns consolidation, emphysema, and fibrosis have been used for this study and the accuracy of the method has been evaluated in terms of mean average precision (mAP) and free receiver operating characteristic (FROC) curve. The model achieved mAP value of 75% and 83% on ZF and VGG networks, respectively. The result obtained shows the effectiveness of the method in the localization of different ILD patterns.
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Acknowledgements
This work is supported by National Institute of Technology, Durgapur by providing the adequate resources and proper scientific environment. Funding is provided by Visvesvaraya PhD scheme of DeitY (Department of Electronics & Information Technology), Govt. of India. The authors are thankful to Medical College Kolkata and EKO DIAGNOSTICS, Kolkata for providing valuable input and feedback for this work.
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Agarwala, S. et al. (2018). Convolutional Neural Networks for Efficient Localization of Interstitial Lung Disease Patterns in HRCT Images. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_2
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