3D multi-view convolutional neural networks for lung nodule classification

Kang, Guixia; Liu, Kui; Hou, Beibei; Zhang, Ningbo

doi:10.1371/journal.pone.0188290

Cited by 140 publications

(106 citation statements)

References 29 publications

(24 reference statements)

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“…AI has recently been applied in the medical field, such as in the diagnosis of pulmonary nodules on computed tomography, 20 diagnosis of skin cancer, 21 classification of retinal lesions based on fundus photographs, 22 endoscopic imaging diagnosis, detection of gastric cancer, 13 evaluation of Helicobacter pylori infection of the stomach, 14 classification of colon polyps, 15 and automatic classification of regions in the upper gastrointestinal endoscopic images. 23 Diagnosis of prostate cancer lesions on prostate magnetic resonance imaging has been attempted in the field of urology, 24 but only one study from the urologic endoscopy field has been reported.…”

Section: Discussionmentioning

confidence: 99%

Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence

Ikeda

Nosato

Kochi

et al. 2020

Journal of Endourology

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Introduction: Nonmuscle-invasive bladder cancer has a relatively high postoperative recurrence rate despite the implementation of conventional treatment methods. Cystoscopy is essential for diagnosing and monitoring bladder cancer, but lesions are overlooked while using white-light imaging. Using cystoscopy, tumors with a small diameter; flat tumors, such as carcinoma in situ; and the extent of flat lesions associated with the elevated lesions are difficult to identify. In addition, the accuracy of diagnosis and treatment using cystoscopy varies according to the skill and experience of physicians. Therefore, to improve the quality of bladder cancer diagnosis, we aimed to support the cystoscopic diagnosis of bladder cancer using artificial intelligence (AI). Materials and Methods: A total of 2102 cystoscopic images, consisting of 1671 images of normal tissue and 431 images of tumor lesions, were used to create a dataset with an 8:2 ratio of training and test images. We constructed a tumor classifier based on a convolutional neural network (CNN). The performance of the trained classifier was evaluated using test data. True-positive rate and false-positive rate were plotted when the threshold was changed as the receiver operating characteristic (ROC) curve. Results: In the test data (tumor image: 87, normal image: 335), 78 images were true positive, 315 true negative, 20 false positive, and 9 false negative. The area under the ROC curve was 0.98, with a maximum Youden index of 0.837, sensitivity of 89.7%, and specificity of 94.0%. Conclusion: By objectively evaluating the cystoscopic image with CNN, it was possible to classify the image, including tumor lesions and normality. The objective evaluation of cystoscopic images using AI is expected to contribute to improvement in the accuracy of the diagnosis and treatment of bladder cancer.

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Section: Discussionmentioning

confidence: 99%

Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence

Ikeda

Nosato

Kochi

et al. 2020

Journal of Endourology

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“…al [173] used specialized CNN architectures like ResNet for detecting malarial parasites in thin blood smear images. Kang et al [174] improved the performance of 2D CNN by using a 3D multi-view CNN for lung nodule classification using spatial contextual information with the help of 3D Inception-ResNet architecture.…”

Section: Medical Image Processingmentioning

confidence: 99%

A review of deep learning with special emphasis on architectures, applications and recent trends

Sengupta

Basak

Saikia

et al. 2020

Knowledge-Based Systems

303

143

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Deep learning has solved a problem that as little as five years ago was thought by many to be intractable -the automatic recognition of patterns in data; and it can do so with an accuracy that often surpasses that of human beings. It has solved problems beyond the realm of traditional, hand-crafted machine learning algorithms and captured the imagination of practitioners trying to make sense out of the flood of data that now inundates our society. As public awareness of the efficacy of deep learning increases so does the desire to make use of it. But even for highly trained professionals it can be daunting to approach the rapidly increasing body of knowledge produced by experts in the field. Where does one start? How does one determine if a particular Deep Learning model is applicable to their problem? How does one train and deploy such a network? A primer on the subject can be a good place to start. With that in mind, we present an overview of some of the key multilayer artificial neural networks that comprise deep learning. We also discuss some new automatic architecture optimization protocols that use multi-agent approaches. Further, since guaranteeing system uptime is becoming critical to many computer applications, we include a section on using neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology: anomalous behavior detection in financial applications or in financial time-series forecasting, predictive and prescriptive analytics, medical image processing and analysis and power systems research. The thrust of this review is to outline emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to use deep learning in their work for what it does best: statistical pattern recognition with unparalleled learning capacity with the ability to scale with information.

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“…DeepLung [36] uses combination of dual path network [37] and gradient boosting machine to classify lung nodules. [38] adopts Multi-View-One-Network strategy in 3D CNN architecture for lung nodule classification, and realize 3D inception and 3D Inception-ResNet for this classification task.…”

Section: Lung Nodule Ct Image Classification Algorithmsmentioning

confidence: 99%

Application of Modified Inception-ResNet and CondenseNet in Lung Nodule Classification

Fu¹

2019

Proceedings of the 3rd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Nowadays lung cancer is one of the most vital malignancies to human health. Restricted by current medical technology, the best solution to lung cancer is still early diagnosis and targeted treatment. Lung nodule is early clinical sign of lung cancer, and low-dose spiral computed tomography is widely considered to be the most effective approach for lung cancer early screening. Through increasing accuracy and stability of diagnosis, CAD can significantly improve quality and efficiency of medical image analysis, reduce the chance of wrong diagnosis caused by subjective factors and missed diagnosis. With rapid development of CNN in image processing, there emerge many kinds of CNN architectures which achieve outstanding performance in image classification. We select Inception-ResNet and CondenseNet as candidate networks due to their outstanding classification performance in ImageNet. Consider reliance among feature map channels and 3D nature of CT scans, 3D-SE-IRNet and 3D-SE-IRNet were designed to further improve accuracy of networks. Results of our experiment prove good performance of CNNs in lung nodule CT scans classification. What's more, introducing self-mechanism and 3D convolution can significantly improve network's accuracy.

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3D multi-view convolutional neural networks for lung nodule classification

Cited by 140 publications

References 29 publications

Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence

Support System of Cystoscopic Diagnosis for Bladder Cancer Based on Artificial Intelligence

A review of deep learning with special emphasis on architectures, applications and recent trends

Application of Modified Inception-ResNet and CondenseNet in Lung Nodule Classification

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