Use data augmentation for a deep learning classification model with chest X-ray clinical imaging featuring coal workers' pneumoconiosis

Dong, Hantian; Zhu, Bin; Zhang, Xinri; Kong, Xiaomei

doi:10.1186/s12890-022-02068-x

Cited by 16 publications

(7 citation statements)

References 37 publications

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“…Recently, an increasing number of studies have focused on using a CNN model to screen patients with pneumoconiosis in DR. ( 21 – 27 ) Zheng et al ( 21 ) applied the transfer learning of LeNet, AlexNet, and three versions of GoogleNet to a pneumoconiosis chest radiograph dataset for the CAD of coal workers with pneumoconiosis. Integrated GoogleNetCF outperformed the remaining models on this dataset (its accuracy was 71.6–93.88%).…”

Section: Discussionmentioning

confidence: 99%

Deep convolutional network-based chest radiographs screening model for pneumoconiosis

Li,

Xu,

Yan

et al. 2024

Front. Med.

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BackgroundPneumoconiosis is the most important occupational disease all over the world, with high prevalence and mortality. At present, the monitoring of workers exposed to dust and the diagnosis of pneumoconiosis rely on manual interpretation of chest radiographs, which is subjective and low efficiency. With the development of artificial intelligence technology, a more objective and efficient computer aided system for pneumoconiosis diagnosis can be realized. Therefore, the present study reported a novel deep learning (DL) artificial intelligence (AI) system for detecting pneumoconiosis in digital frontal chest radiographs, based on which we aimed to provide references for radiologists.MethodsWe annotated 49,872 chest radiographs from patients with pneumoconiosis and workers exposed to dust using a self-developed tool. Next, we used the labeled images to train a convolutional neural network (CNN) algorithm developed for pneumoconiosis screening. Finally, the performance of the trained pneumoconiosis screening model was validated using a validation set containing 495 chest radiographs.ResultsApproximately, 51% (25,435/49,872) of the chest radiographs were labeled as normal. Pneumoconiosis was detected in 49% (24,437/49,872) of the labeled radiographs, among which category-1, category-2, and category-3 pneumoconiosis accounted for 53.1% (12,967/24,437), 20.4% (4,987/24,437), and 26.5% (6,483/24,437) of the patients, respectively. The CNN DL algorithm was trained using these data. The validation set of 495 digital radiography chest radiographs included 261 cases of pneumoconiosis and 234 cases of non-pneumoconiosis. As a result, the accuracy of the AI system for pneumoconiosis identification was 95%, the area under the curve was 94.7%, and the sensitivity was 100%.ConclusionDL algorithm based on CNN helped screen pneumoconiosis in the chest radiographs with high performance; thus, it could be suitable for diagnosing pneumoconiosis automatically and improve the efficiency of radiologists.

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Section: Discussionmentioning

confidence: 99%

Deep convolutional network-based chest radiographs screening model for pneumoconiosis

Li,

Xu,

Yan

et al. 2024

Front. Med.

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“…By randomly modifying the input images while preserving their labels, we effectively expanded the variety of the training data. This augmentation approach fortifies the network's capacity to generalize and make accurate predictions when faced with unseen data [11].…”

Section: Data Augmentationmentioning

confidence: 99%

Evaluation of filtering and contrast in X-ray and computerized tomography scan lung classification

Setty,

Mathad,

Shenthar

et al. 2024

IJEECS

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Deep learning provides many convenient methods to help medical practitioners take informed decisions about diverse ailments. The goal of this project is to measure the effectiveness of filters and contrast enhancement techniques qualitatively and quantitatively in classifying lung scan images. Transfer deep learning was used to obtain the necessary results, with DenseNet 121 being the base model. Salt and pepper filter was used to introduce noise, and 3×3 mean and 5×5 mean with contrast limited adaptive histogram equalization (CLAHE) was used to minimize the effect of noise. All layers excluding the rearmost were frozen, and new dense and dropout layers were added to identify features of computerized tomography (CT) scan images of lungs. The resultant models were of comparable accuracy, where the model with no filter gave the accurate results for the given data, and the one using the 5×5 mean filter gave better adaptability in classification of unseen data. The misclassification between normal and pneumonia affected lungs is relatively higher, because of the lack of distinct features between them.

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“…The authors of [ 21 ] analyzed the X-ray image dataset to improve the accuracy of image classification, and in particular, investigated the data augmentation methods in medical datasets. The study of [ 22 ] dealt with the data augmentation techniques in the field of CWP (Coal Workers’ Pneumoconiosis) using X-ray. However, X-ray image recognition researches in one medical domain [ 21 , 22 ] may have performance limitations in direct application to another medical domain such as breast cancer diagnosis, so that additional verification is requested according to the domain change.…”

Section: Related Workmentioning

confidence: 99%

Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition

Kwak¹,

Choi²,

Lee³

2023

Sensors

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This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3), and related loss functions (binary cross entropy, dice Loss, Tversky loss), and data augmentation. As a result of evaluations through the presented methods, when using filter-based data augmentation, ResNet50 showed the best performance in image classification, and UNet showed the best performance in both X-ray image and ultrasound image as image segmentation. When applying the proposed image recognition strategies for the maximal diagnosis accuracy in each medical image data, the accuracy can be improved by 33.3% in image segmentation in X-ray images, 29.9% in image segmentation in ultrasound images, and 22.8% in image classification in histopathology images.

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Use data augmentation for a deep learning classification model with chest X-ray clinical imaging featuring coal workers' pneumoconiosis

Cited by 16 publications

References 37 publications

Deep convolutional network-based chest radiographs screening model for pneumoconiosis

Deep convolutional network-based chest radiographs screening model for pneumoconiosis

Evaluation of filtering and contrast in X-ray and computerized tomography scan lung classification

Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition

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