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2017 | OriginalPaper | Chapter

Deep CNN-Based Method for Segmenting Lung Fields in Digital Chest Radiographs

Authors : Simranpreet Kaur, Rahul Hooda, Ajay Mittal, Akashdeep, Sanjeev Sofat

Published in: Advanced Informatics for Computing Research

Publisher: Springer Singapore

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Abstract

Lung Field Segmentation (LFS) is an indispensable step for detecting austere lung diseases in various computer-aided diagnosis. This paper presents a deep learning-based Convolutional Neural Network (CNN) for segmenting lung fields in chest radiographs. The proposed CNN network consists of three sets of convolutional-layer and rectified linear unit (ReLU) layer, followed by a fully connected layer. At each convolutional layer, 64 filters retrieve the representative features. Japanese Society of Radiological Technology (JSRT) dataset is used for training and validation. Test results have 98.05% average accuracy, 93.4% average overlap, 96.25% average sensitivity, and 98.80% average specificity. The obtained results are promising and better than many of the existing state-of-the-art LFS techniques.

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Metadata
Title
Deep CNN-Based Method for Segmenting Lung Fields in Digital Chest Radiographs
Authors
Simranpreet Kaur
Rahul Hooda
Ajay Mittal
Akashdeep
Sanjeev Sofat
Copyright Year
2017
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-10-5780-9_17

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