Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study

Chae, Kum Ju; Jin, Gong Yong; Ko, Seok‐Bum; Wang, Yi; Zhang, Hao; Choi, Eun Jung; Choi, Hyemi

doi:10.1016/j.acra.2019.05.018

Cited by 28 publications

(20 citation statements)

References 17 publications

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“…Recently, Ardila D, et al achieved end-to-end lung cancer screening on low-dose chest CT with an AUC of 94.4% 29 . Chae KJ, et al successfully used the convolutional neural network to classify small (≤ 2 cm) pulmonary nodules on CT scan images 30 . However, there was rare research being conducted to detect viral pneumonia 11,29,30 .…”

Section: Discussionmentioning

confidence: 99%

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Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Chen

Zhang

et al. 2020

Sci Rep

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Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.

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Section: Discussionmentioning

confidence: 99%

“…Chae KJ, et al successfully used the convolutional neural network to classify small (≤ 2 cm) pulmonary nodules on CT scan images 30 . However, there was rare research being conducted to detect viral pneumonia 11,29,30 . Most previous studies detected pneumonia on X-ray using deep learning while not focused on viral pneumonia.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Chen

Zhang

et al. 2020

Sci Rep

329

229

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“…[27] Chae KJ, et al successfully used the convolutional neural network to classify small (≤2 cm) pulmonary nodules on CT scan images. [28] However, there was rare research being conducted to detect viral pneumonia. [11,27,28] In previous work, our group succeeded in recruiting deep learning in minor lesion detection and real-time assistance to doctors in gastrointestinal endoscopy.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study

Chen

Zhang

et al. 2020

Preprint

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“…In this study, the ratio of data sets was not 80:20, and the number of data sets was one. However, since some papers use a 90:10 ratio of data sets [22,23] and others use various ratios [1,6,24], this is not considered a problem. As for the number of data sets, it is not a problem since there are papers that validate with only one [3,4,25].…”

Section: Discussionmentioning

confidence: 99%

“…Image classification is a typical technology of image analysis that uses artificial intelligence. In computed tomography (CT) images, it has been used to classify pulmonary nodules [1,2], slice positions [3,4], and calcaneal fractures [5,6]. Both AlexNet [7] and ResNet [8] are examples of image classification models.…”

Section: Introductionmentioning

confidence: 99%

Improvement in the Convolutional Neural Network for Computed Tomography Images

et al. 2021

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Background and purpose. This study evaluated a modified specialized convolutional neural network (CNN) to improve the accuracy of medical images. Materials and Methods. We defined computed tomography (CT) images as belonging to one of the following 10 classes: head, neck, chest, abdomen, and pelvis with and without contrast media, with 10,000 images per class. We modified the CNN based on the AlexNet with an input size of 512 × 512. We resized the filter sizes of the convolution layer and max pooling. Using these modified CNNs, various models were created and evaluated. The improved CNN was evaluated to classify the presence or absence of the pancreas in the CT images. We compared the overall accuracy, which was calculated from images not used for training, to that of the ResNet. Results. The overall accuracies of the most improved CNN and ResNet in the 10 classes were 94.8% and 89.3%, respectively. The filter sizes of the improved CNN for the convolution layer were (13, 13), (7, 7), (5, 5), (5, 5), and (5, 5) in order from the first layer, and that of max-pooling was (7, 7). The calculation times of the most improved CNN and ResNet were 56 and 120 min, respectively. Regarding the classification of the pancreas, the overall accuracies of the most improved CNN and ResNet were 75.75% and 58.25%, respectively. The calculation times of the most improved CNN and ResNet were 36 and 55 min, respectively. Conclusion. By optimizing the filter size of the convolution layer and max-pooling of 512 × 512 images, we quickly obtained a highly accurate medical image classification model. This improved CNN can be useful for classifying lesions and anatomies for related diagnostic aid applications.

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Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study

Cited by 28 publications

References 17 publications

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: a prospective study

Improvement in the Convolutional Neural Network for Computed Tomography Images

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