Study on the Detection of Pulmonary Nodules in CT Images Based on Deep Learning

Li, Gai; Zhou, Wei; Chen, Weibin; Sun, Fenglian; Fu, Yu; Gong, Fengling; Zhang, Huiying

doi:10.1109/access.2020.2984381

Cited by 16 publications

(6 citation statements)

References 40 publications

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“…The accuracy values of AI services for analyzing CT/LDCT and detecting malignant lesions match the literature data: sensitivity -73.0-100.0%, specificity -71.0-89.0%, AUC -0.86-97.6 [31][32][33][34][35][36] . In a retrospective study on a limited sample, a consistency of AI technologies and medical experts in diagnosing lung nodules with LDCT was established with Cohen's kappa of 0.846 31 .…”

Section: Discussionsupporting

confidence: 82%

Diagnostic Accuracy of Artificial Intelligence for Analysis of 1.3 Million Medical Imaging Studies: The Moscow Experiment on Computer Vision Technologies

Morozov,

Vladzymyrskyy,

Ledikhova

et al. 2023

Preprint

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Objective: to assess the diagnostic accuracy of services based on computer vision technologies at the integration and operation stages in Moscow's Unified Radiological Information Service (URIS). Methods: this is a multicenter diagnostic study of artificial intelligence (AI) services with retrospective and prospective stages. The minimum acceptable criteria levels for the index test were established, justifying the intended clinical application of the investigated index test. The Experiment was based on the infrastructure of the URIS and United Medical Information and Analytical System (UMIAS) of Moscow. Basic functional and diagnostic requirements for the artificial intelligence services and methods for monitoring technological and diagnostic quality were developed. Diagnostic accuracy metrics were calculated and compared. Results: based on the results of the retrospective study, we can conclude that AI services have good result reproducibility on local test sets. The highest and at the same time most balanced metrics were obtained for AI services processing CT scans. All AI services demonstrated a pronounced decrease in diagnostic accuracy in the prospective study. The results indicated a need for further refinement of AI services with additional training on the Moscow population datasets. Conclusions: the diagnostic accuracy and reproducibility of AI services on the reference data are sufficient, however, they are insufficient on the data in routine clinical practice. The AI services that participated in the experiment require a technological improvement, additional training on Moscow population datasets, technical and clinical trials to get a status of a medical device.

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Section: Discussionsupporting

confidence: 82%

Diagnostic Accuracy of Artificial Intelligence for Analysis of 1.3 Million Medical Imaging Studies: The Moscow Experiment on Computer Vision Technologies

Morozov,

Vladzymyrskyy,

Ledikhova

et al. 2023

Preprint

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“…(2) e Dropout function is used during training, through which the parameters of some neurons can be randomly ignored, i.e., the activity of some neurons is suppressed, and this measure can also avoid overfitting of the model [8]. (3) Applying overlapping pooling layers to the convolutional neural network can not only significantly improve the computational speed compared with the previous average pooling layers but also-to a certain extent-reduce the overfitting of the model [9]. e overfitting of the model can be reduced to a certain extent.…”

Section: State Of the Artmentioning

confidence: 99%

Application of Recurrent Neural Network Algorithm in Intelligent Detection of Clinical Ultrasound Images of Human Lungs

Jia

2022

Computational Intelligence and Neuroscience

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Lung ultrasound has great application value in the differential diagnosis of pulmonary exudative lesions. It has good sensitivity and specificity for the diagnosis of various pulmonary diseases in neonates and children. It is believed that it can replace chest CT examination. It is routinely used for the diagnosis of pulmonary diseases in emergency critical care medicine. However, the interpretation of the impact of ultrasound on the human lungs relies heavily on experienced physicians, which greatly restricts the interpretation efficiency of the impact of ultrasound. In order to improve the efficiency of monitoring and interpretation of the impact of ultrasound, this paper proposes an intelligent detection algorithm for human lung clinical ultrasound images based on recurrent neural network. Transfer learning is used to replace the fully connected layer of the VGG16 model and improve the loss function, so that the same the Euclidean distance between category images can be reduced, and the Euclidean distance between different categories of images can be increased, enhancing the resolution of the entire model, thereby achieving better image feature extraction results. The experimental results show that the algorithm proposed in this paper can surpass the doctor’s level in the identification of various lung diseases.

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“…Generally, thresholding, component analysis, region growing, morphological operations, and filtering [19], [22], [26], [51]- [52], [62], [67]- [68], [96], [109] are often used as rule-based approaches in preprocessing medical images. Thresholding and component analysis are the most effective and quick ways to approximately separate lung volume from distracting components.…”

Section: A Preprocessingmentioning

confidence: 99%

A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

Cao

et al. 2020

IEEE Access

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Lung cancer is one of the malignant tumor diseases with the fastest increase in morbidity and mortality, which poses a great threat to human health. Low-Dose Computed Tomography (LDCT) screening has been proved as a practical technique for improving the accuracy of pulmonary nodule detection and classification at early cancer diagnosis, which contributes to mortality reduction. Therefore, with the explosive growth of CT data, it is of great clinical significance to exploit an effective Computer-Aided Diagnosis (CAD) system for radiologists on automatic nodule analysis. In this paper, a comprehensive review of the application and development of CAD systems is presented. The experimental benchmarks for nodule analysis are first described and summarized, covering public datasets of lung CT scans, commonly used evaluation metrics, and various medical competitions. We then introduce the main structure of a CAD system and present some efficient methodologies. Due to the extensive use of Convolutional Neural Network (CNN)based methods in pulmonary nodule investigations recently, we summarized the advantages of CNNs over traditional image processing methods. Besides, we mainly select the CAD systems developed by state-of-theart CNNs with excellent performance and analyze their objectives, algorithms as well as results. Finally, research trends, existing challenges, and future directions in this field are discussed.

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Study on the Detection of Pulmonary Nodules in CT Images Based on Deep Learning

Cited by 16 publications

References 40 publications

Diagnostic Accuracy of Artificial Intelligence for Analysis of 1.3 Million Medical Imaging Studies: The Moscow Experiment on Computer Vision Technologies

Diagnostic Accuracy of Artificial Intelligence for Analysis of 1.3 Million Medical Imaging Studies: The Moscow Experiment on Computer Vision Technologies

Application of Recurrent Neural Network Algorithm in Intelligent Detection of Clinical Ultrasound Images of Human Lungs

A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

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