Computer-aided classification of lung nodules on computed tomography images via deep learning technique

Hua, Kai-Lung; Hsu, Che-Hao; Hidayati, Shintami Chusnul; Cheng, Winton; Chen, Yu-Jen

doi:10.2147/ott.s80733

Cited by 243 publications

(85 citation statements)

References 17 publications

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“…In comparison, malignant nodules are more likely to present themselves with irregular shapes, rougher surfaces, and speckled patterns 9 . A superior spatial resolution could not only improve the classification of small pulmonary nodules (≥4 mm) during the clinical routine 10 but also improve the performance of software-based classification systems 11 . A different example is the early diagnosis of chronic obstructive pulmonary disease (COPD), which is gaining in importance worldwide 12 .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a preclinical photon-counting CT prototype for pulmonary imaging

Kopp

Daerr

Si‐Mohamed

et al. 2018

Sci Rep

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The purpose of this study was to investigate a preclinical spectral photon-counting CT (SPCCT) prototype compared to conventional CT for pulmonary imaging. A custom-made lung phantom, including nodules of different sizes and shapes, was scanned with a preclinical SPCCT and a conventional CT in standard and high-resolution (HR-CT) mode. Volume estimation was evaluated by linear regression. Shape similarity was evaluated with the Dice similarity coefficient. Spatial resolution was investigated via MTF for each imaging system. In-vivo rabbit lung images from the SPCCT system were subjectively reviewed. Evaluating the volume estimation, linear regression showed best results for the SPCCT compared to CT and HR-CT with a root mean squared error of 21.3 mm3, 28.5 mm3 and 26.4 mm3 for SPCCT, CT and HR-CT, respectively. The Dice similarity coefficient was superior for SPCCT throughout nodule shapes and all nodule sizes (mean, SPCCT: 0.90; CT: 0.85; HR-CT: 0.85). 10% MTF improved from 10.1 LP/cm for HR-CT to 21.7 LP/cm for SPCCT. Visual investigation of small pulmonary structures was superior for SPCCT in the animal study. In conclusion, the SPCCT prototype has the potential to improve the assessment of lung structures due to higher resolution compared to conventional CT.

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Section: Introductionmentioning

confidence: 99%

Evaluation of a preclinical photon-counting CT prototype for pulmonary imaging

Kopp

Daerr

Si‐Mohamed

et al. 2018

Sci Rep

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“… 2017 ), and lung nodules (Hua et al. 2015 ). However, in the field of orthopedic surgery and traumatology, trials are very scarce despite its importance to public health.…”

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confidence: 99%

Automated detection and classification of the proximal humerus fracture by using deep learning algorithm

Chung¹,

Han

Lee³

et al. 2018

Acta Orthopaedica

355

240

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Background and purpose — We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs.Patients and methods — 1,891 images (1 image per person) of normal shoulders (n = 515) and 4 proximal humerus fracture types (greater tuberosity, 346; surgical neck, 514; 3-part, 269; 4-part, 247) classified by 3 specialists were evaluated. We trained a deep convolutional neural network (CNN) after augmentation of a training dataset. The ability of the CNN, as measured by top-1 accuracy, area under receiver operating characteristics curve (AUC), sensitivity/specificity, and Youden index, in comparison with humans (28 general physicians, 11 general orthopedists, and 19 orthopedists specialized in the shoulder) to detect and classify proximal humerus fractures was evaluated.Results — The CNN showed a high performance of 96% top-1 accuracy, 1.00 AUC, 0.99/0.97 sensitivity/specificity, and 0.97 Youden index for distinguishing normal shoulders from proximal humerus fractures. In addition, the CNN showed promising results with 65–86% top-1 accuracy, 0.90–0.98 AUC, 0.88/0.83–0.97/0.94 sensitivity/specificity, and 0.71–0.90 Youden index for classifying fracture type. When compared with the human groups, the CNN showed superior performance to that of general physicians and orthopedists, similar performance to orthopedists specialized in the shoulder, and the superior performance of the CNN was more marked in complex 3- and 4-part fractures.Interpretation — The use of artificial intelligence can accurately detect and classify proximal humerus fractures on plain shoulder AP radiographs. Further studies are necessary to determine the feasibility of applying artificial intelligence in the clinic and whether its use could improve care and outcomes compared with current orthopedic assessments.

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“…In Reference [33], the authors use two models based on Deep Belief Networks (DBN) and Convolutional Neural Networks (CNN), respectively. The authors used the LIDC dataset where the training samples were resized to 32 × 32 ROIs.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

“…Deep belief network learning framework, illustration of the concept of training a nodule classifier as in Reference[33].…”

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confidence: 99%

Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans

Riquelme

Akhloufi

2020

126

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Detecting malignant lung nodules from computed tomography (CT) scans is a hard and time-consuming task for radiologists. To alleviate this burden, computer-aided diagnosis (CAD) systems have been proposed. In recent years, deep learning approaches have shown impressive results outperforming classical methods in various fields. Nowadays, researchers are trying different deep learning techniques to increase the performance of CAD systems in lung cancer screening with computed tomography. In this work, we review recent state-of-the-art deep learning algorithms and architectures proposed as CAD systems for lung cancer detection. They are divided into two categories—(1) Nodule detection systems, which from the original CT scan detect candidate nodules; and (2) False positive reduction systems, which from a set of given candidate nodules classify them into benign or malignant tumors. The main characteristics of the different techniques are presented, and their performance is analyzed. The CT lung datasets available for research are also introduced. Comparison between the different techniques is presented and discussed.

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Computer-aided classification of lung nodules on computed tomography images via deep learning technique

Cited by 243 publications

References 17 publications

Evaluation of a preclinical photon-counting CT prototype for pulmonary imaging

Evaluation of a preclinical photon-counting CT prototype for pulmonary imaging

Automated detection and classification of the proximal humerus fracture by using deep learning algorithm

Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans

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