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

Hashimoto, Noriaki; Suzuki, Kenji; Liu, Junchi; Hirano, Yasushi; MacMahon, Heber; Shoji, K.

doi:10.1117/12.2293550

Cited by 5 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shin et al 9 used the learning transferred from ImageNet, 10 a database of world pictures for visual object recognition, to different CNN architectures to classify healthy, emphysema, GG, fibrosis, MN, and CD patterns in DLDs, achieving an accuracy of 79.00%. Hashimoto et al 11 built an ensemble of two deep DCNN architectures, which one responsible for learning a correct classification between CD and NL and GG and NL. They achieved a classification accuracy of 93.30%.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases

et al. 2018

View full text Add to dashboard Cite

Computational Intelligence Re-meets Medical Image Processing A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration Background Diffuse lung diseases (DLDs) are a diverse group of pulmonary disorders, characterized by inflammation of lung tissue, which may lead to permanent loss of the ability to breathe and death. Distinguishing among these diseases is challenging to physicians due their wide variety and unknown causes. Computer-aided diagnosis (CAD) is a useful approach to improve diagnostic accuracy, by combining information provided by experts with Machine Learning (ML) methods. Objectives Exploring the potential of dimensionality reduction combined with ML methods for diagnosis of DLDs; improving the classification accuracy over state-of-the-art methods. Methods A data set composed of 3252 regions of interest (ROIs) was used, from which 28 features were extracted per ROI. We used Principal Component Analysis, Linear Discriminant Analysis, and Stepwise Selection – Forward, Backward, and Forward-Backward to reduce feature dimensionality. The feature subsets obtained were used as input to the following ML methods: Support Vector Machine, Gaussian Mixture Model, k-Nearest Neighbor, and Deep Feedforward Neural Network. We also applied a Deep Convolutional Neural Network directly to the ROIs. Results We achieved the maximum reduction from 28 to 5 dimensions using LDA. The best classification results were obtained by DFNN, with 99.60% of overall accuracy. Conclusions This work contributes to the analysis and selection of features that can efficiently characterize the DLDs studied.

show abstract

Section: Introductionmentioning

confidence: 99%

Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases

et al. 2018

View full text Add to dashboard Cite

show abstract

“…A number of studies regarding automated assessment of DLDs have been conducted in various context including image level classification and semantic segmentation. There are several kinds of supervised methods for automated assessment of DLDs including fully-supervised, [2][3][4] semi-supervised, 5 weakly supervised, 6 and unsupervised. 7 Machine learning techniques are widely used for semantic segmentation since they are capable of learning complicated texture patterns and often outperform hand-crafted algorithms.…”

Section: Introductionmentioning

confidence: 99%

Weak supervision in convolutional neural network for semantic segmentation of diffuse lung diseases using partially annotated dataset

Suzuki

Yamagata

Yanagawa

et al. 2020

Medical Imaging 2020: Computer-Aided Diagnosis

Self Cite

View full text Add to dashboard Cite

Computer-aided diagnosis system for diffuse lung diseases (DLDs) is necessary for the objective assessment of the lung diseases. In this paper, we develop semantic segmentation model for 5 kinds of DLDs. DLDs considered in this work are consolidation, ground glass opacity, honeycombing, emphysema, and normal. Convolutional neural network (CNN) is one of the most promising technique for semantic segmentation among machine learning algorithms. While creating annotated dataset for semantic segmentation is laborious and time consuming, creating partially annotated dataset, in which only one chosen class is annotated for each image, is easier since annotators only need to focus on one class at a time during the annotation task. In this paper, we propose a new weak supervision technique that effectively utilizes partially annotated dataset. The experiments using partially annotated dataset composed 372 CT images demonstrated that our proposed technique significantly improved segmentation accuracy.

show abstract