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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Kaur, S., Hooda, R., Mittal, A., Akashdeep, Sofat, S. (2017). Deep CNN-Based Method for Segmenting Lung Fields in Digital Chest Radiographs. In: Singh, D., Raman, B., Luhach, A., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2017. Communications in Computer and Information Science, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-10-5780-9_17
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