Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs

Devnath, Liton; Luo, Siwei; Summons, Peter; Wang, Dadong

doi:10.1016/j.compbiomed.2020.104125

Cited by 37 publications

(26 citation statements)

References 20 publications

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“…In addition, a comparison with state-of-the-art works is given in Table 2 , where 11 different algorithms are adopted for pneumoconiosis diagnosis [ 30 ]. The binary classification results of our proposed method significantly outperform those found in the aforementioned works, where the second-best result in these works, obtained from a general neural network classifier, is only 83%.…”

Section: Discussionmentioning

confidence: 99%

Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning

et al. 2021

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Purpose The objective of this study is to construct a computer aided diagnosis system for normal people and pneumoconiosis using X-raysand deep learning algorithms. Materials and methods 1760 anonymous digital X-ray images of real patients between January 2017 and June 2020 were collected for this experiment. In order to concentrate the feature extraction ability of the model more on the lung region and restrain the influence of external background factors, a two-stage pipeline from coarse to fine was established. First, the U-Net model was used to extract the lung regions on each sides of the collection images. Second, the ResNet-34 model with transfer learning strategy was implemented to learn the image features extracted in the lung region to achieve accurate classification of pneumoconiosis patients and normal people. Results Among the 1760 cases collected, the accuracy and the area under curve of the classification model were 92.46% and 89% respectively. Conclusion The successful application of deep learning in the diagnosis of pneumoconiosis further demonstrates the potential of medical artificial intelligence and proves the effectiveness of our proposed algorithm. However, when we further classified pneumoconiosis patients and normal subjects into four categories, we found that the overall accuracy decreased to 70.1%. We will use the CT modality in future studies to provide more details of lung regions.

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Section: Discussionmentioning

confidence: 99%

Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning

et al. 2021

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“…DenseNet is combined with other methods; sixth, Devnath et al [ 94 ] used CheXNet to extract multilevel features of chest X-ray images, mixed SVM and CNN aggregation methods, transition layer low-level features, and deep-level features mapped to high-dimensional space after merging dichotomous classification. Seventh, Turkoglu [ 95 ] proposed a multicore extreme learning machine (ELM) method to extract deep-level features of chest CT with pretrained DenseNet201, and ELM classifier classified features with different activation methods to predict final presence of neo-coronary pneumonia by majority voting.…”

Section: Application Of Densenet In Medical Image Analysismentioning

confidence: 99%

Dense Convolutional Network and Its Application in Medical Image Analysis

Tao

XinYu

et al. 2022

BioMed Research International

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Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.

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“…In the most recent computer vision applications, CNN has been used in many fields, including medical image analysis, which achieved outstanding state-of-the-art performances [ 98 , 99 ]. This study only found eight research articles based on the use of CNN to detect CWP in CXR in which non-texture features were extracted from the lung image [ 49 , 67 , 68 , 69 , 70 , 71 , 72 , 73 ]. Zheng et al [ 73 ] investigated the CAD of CWP with the CXR films dataset, which indicated that traditional texture analysis is not enough to diagnose.…”

Section: Analysis Of Returned Articlesmentioning

confidence: 99%

“…They proposed only one CNN model, Inception-V3, for automated feature extraction and classification of pneumoconiosis in digital CXR, and compared this with the performances of two certified radiologists [ 70 ]. Recently, Devnath et al [ 68 ] proposed an innovative method to detect CWP in CXR for a small dataset. They used a CNN model to extract multi-level and multidimensional features from the proposed architecture [ 112 ].…”

Section: Analysis Of Returned Articlesmentioning

confidence: 99%

“…For the period 2019–2021, this review only found eights studies that proposed using deep convolutional neural network (CNN) models to classify CWP (black lung disease) in CXR [ 49 , 67 , 68 , 69 , 70 , 71 , 72 , 73 ]. They used different pre-trained deep learning models for non-textured feature extraction then applied a fully connected layer with binary classifier for normal or abnormal (black lung) classification.…”

Section: Analysis Of Returned Articlesmentioning

confidence: 99%

See 1 more Smart Citation

Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review

Devnath

Summons

Luo

et al. 2022

IJERPH

Self Cite

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Computer-aided diagnostic (CAD) systems can assist radiologists in detecting coal workers’ pneumoconiosis (CWP) in their chest X-rays. Early diagnosis of the CWP can significantly improve workers’ survival rate. The development of the CAD systems will reduce risk in the workplace and improve the quality of chest screening for CWP diseases. This systematic literature review (SLR) amis to categorise and summarise the feature extraction and detection approaches of computer-based analysis in CWP using chest X-ray radiographs (CXR). We conducted the SLR method through 11 databases that focus on science, engineering, medicine, health, and clinical studies. The proposed SLR identified and compared 40 articles from the last 5 decades, covering three main categories of computer-based CWP detection: classical handcrafted features-based image analysis, traditional machine learning, and deep learning-based methods. Limitations of this review and future improvement of the review are also discussed.

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Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs

Cited by 37 publications

References 20 publications

Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning

Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning

Dense Convolutional Network and Its Application in Medical Image Analysis

Computer-Aided Diagnosis of Coal Workers’ Pneumoconiosis in Chest X-ray Radiographs Using Machine Learning: A Systematic Literature Review

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