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Proceedings Paper

Deep neural network convolution (NNC) for three-class classification of diffuse lung disease opacities in high-resolution CT (HRCT): consolidation, ground-glass opacity (GGO), and normal opacity
Author(s): Noriaki Hashimoto; Kenji Suzuki; Junchi Liu; Yasushi Hirano; Heber MacMahon; Shoji Kido
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Paper Abstract

Consolidation and ground-glass opacity (GGO) are two major types of opacities associated with diffuse lung diseases. Accurate detection and classification of such opacities are crucially important in the diagnosis of lung diseases, but the process is subjective, and suffers from interobserver variability. Our study purpose was to develop a deep neural network convolution (NNC) system for distinguishing among consolidation, GGO, and normal lung tissue in high-resolution CT (HRCT). We developed ensemble of two deep NNC models, each of which was composed of neural network regression (NNR) with an input layer, a convolution layer, a fully-connected hidden layer, and a fully-connected output layer followed by a thresholding layer. The output layer of each NNC provided a map for the likelihood of being each corresponding lung opacity of interest. The two NNC models in the ensemble were connected in a class-selection layer. We trained our NNC ensemble with pairs of input 2D axial slices and “teaching” probability maps for the corresponding lung opacity, which were obtained by combining three radiologists’ annotations. We randomly selected 10 and 40 slices from HRCT scans of 172 patients for each class as a training and test set, respectively. Our NNC ensemble achieved an area under the receiver-operating-characteristic (ROC) curve (AUC) of 0.981 and 0.958 in distinction of consolidation and GGO, respectively, from normal opacity, yielding a classification accuracy of 93.3% among 3 classes. Thus, our deep-NNC-based system for classifying diffuse lung diseases achieved high accuracies for classification of consolidation, GGO, and normal opacity.

Paper Details

Date Published: 27 February 2018
PDF: 7 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057536 (27 February 2018); doi: 10.1117/12.2293550
Show Author Affiliations
Noriaki Hashimoto, Yamaguchi Univ. (Japan)
Kenji Suzuki, Illinois Institute of Technology (United States)
Tokyo Institute of Technology (Japan)
Junchi Liu, Illinois Institute of Technology (United States)
Yasushi Hirano, Yamaguchi Univ. (Japan)
Heber MacMahon, The Univ. of Chicago (United States)
Shoji Kido, Yamaguchi Univ. (Japan)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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