2D CNN versus 3D CNN for false-positive reduction in lung cancer screening

Yu, Juezhao; Yang, Binyao; Wang, Jing; Leader, Joseph K.; Wilson, David O.; Pu, Jiantao

doi:10.1117/1.jmi.7.5.051202

Cited by 44 publications

(26 citation statements)

References 45 publications

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“…Firstly, Dice, sensitivity, and PPV were selected to perform quantitative evaluation of 2D CNN [18], 3D CNN [19], U-Net [20], and the model proposed in this paper. e results is shown in Figure 4.…”

Section: Test Of Ct Image Segmentation Modelmentioning

confidence: 99%

Computed Tomography Images under Deep Learning Algorithm in the Diagnosis of Perioperative Rehabilitation Nursing for Patients with Lung Cancer

Sha¹,

Huang²,

Yu³

et al. 2022

Scientific Programming

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This study aim was to explore the application effect of computed tomography (CT) image segmentation based on deep learning algorithm in the diagnosis of lung cancer. In this study, a two-dimensional (2D) convolutional neural network (CNN) and three-dimensional (3D) CNN fusion model was constructed firstly. Subsequently, 60 patients with lung cancer were randomly divided into a control group and an intervention group to receive perioperative routine nursing and rehabilitation nursing, respectively. The results revealed that the Dice value (0.876), sensitivity (0.849), and positive predictive value (PPV) (0.875) of the hybrid feature fusion model (HFFM) constructed in this study for lung cancer CT image segmentation were higher than those of other models, and the accuracy rate for lung cancer diagnosis was 96.7%. After nursing intervention, the partial arterial oxygen pressure (PaO2) and partial arterial carbon dioxide pressure (PaCO2) in the control group were 80.54 mmHg and 39.81 mmHg, respectively, while those in the intervention group were 83.09 mmHg and 36.75 mmHg, respectively. After intervention, the maximal voluntary ventilation (MVV) %, forced vital capacity (FVC) %, and forced expiratory volume in 1 sec (FEV1) %) in the intervention group were 76.03%, 82.14%, and 89.76%, respectively. It suggested that compared with the control group, the pulmonary function indexes of the intervention group improved significantly after nursing intervention ( P < 0.05). In summary, the HFFM constructed in this study can be used for segmentation and classification of CT images of lung cancer patients, which can improve the accuracy of diagnosis and help improve the lung function and quality of life of patients.

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Section: Test Of Ct Image Segmentation Modelmentioning

confidence: 99%

Computed Tomography Images under Deep Learning Algorithm in the Diagnosis of Perioperative Rehabilitation Nursing for Patients with Lung Cancer

Sha¹,

Huang²,

Yu³

et al. 2022

Scientific Programming

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“…In this paper, We assess the effectiveness of 3DCNN on locating radial artery upon radius at the wrist by medical video data from infrared camera. The traditional 2DCNN locating model focuses on analyzing a single picture [26], by extracting spatial feature from convolutional layers, 2DCNN can learn relevant spatial information features such as wrist skin texture [27], therefore, for most standard groups, 2DCNN can achieve nice localization results. However, from an anatomical point of view, the interlaced meridians of the body's wrists are complicated [28].…”

Section: The Effection Of Locationmentioning

confidence: 99%

Palpation Localization of Radial Artery Based on 3-Dimensional Convolutional Neural Networks

Chen

Huang²,

Yang³

et al. 2021

Preprint

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Palpation localization is essential for detecting physiological parameters of the radial artery for pulse diagnosis of Traditional Chinese Medicine (TCM). Detecting signal or applying pressure at the wrong location can seriously affect the measurement of pulse waves and result in misdiagnosis. In this paper, we propose an effective and high accuracy regression model using 3-dimensional convolution neural networks (CNN) processing near-infrared picture sequences to locate radial artery upon radius at the wrist. Comparing with early studies using 2-dimensional models, 3D CNN introduces temporal features with the third dimension to leverage pulsation rhythms. The model had achieved superior performance accuracy as 0.87 within 50 pixels at picture resolution of 2048*1088. Model visualization shows that the additional dimension of the temporal convolution highlights dynamic changes within image sequences. This study presents the great potential of our constructed model to be applied in real wrist palpation location scenarios to bring the key convenience for pulse diagnosis.

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“…CNN algorithms were employed to filter and reconstruct low-dose CT images, coupling diagnosis accuracy with a less invasive approach. [25,26] Alongside CNNs, RNNs have been used for image diagnostics combining multiple techniques (e.g., magnetic resonance imaging coupled with positron emission tomography), thus revealing the power of such models in extracting valuable information from multimodal medical data. [27] Despite these promising results, the full benefits of the combination between AI and photonics have not yet been reaped.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Classical and Quantum Photonics

Vernuccio

Bresci

Cimini

et al. 2022

Laser & Photonics Reviews

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The last decades saw a huge rise of artificial intelligence (AI) as a powerful tool to boost industrial and scientific research in a broad range of fields. AI and photonics are developing a promising two‐way synergy: on the one hand, AI approaches can be used to control a number of complex linear and nonlinear photonic processes, both in the classical and quantum regimes; on the other hand, photonics can pave the way for a new class of platforms to accelerate AI‐tasks. This review provides the reader with the fundamental notions of machine learning (ML) and neural networks (NNs) and presents the main AI applications in the fields of spectroscopy and chemometrics, computational imaging (CI), wavefront shaping and quantum optics. The review concludes with an overview of future developments of the promising synergy between AI and photonics.

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2D CNN versus 3D CNN for false-positive reduction in lung cancer screening

Cited by 44 publications

References 45 publications

Computed Tomography Images under Deep Learning Algorithm in the Diagnosis of Perioperative Rehabilitation Nursing for Patients with Lung Cancer

Computed Tomography Images under Deep Learning Algorithm in the Diagnosis of Perioperative Rehabilitation Nursing for Patients with Lung Cancer

Palpation Localization of Radial Artery Based on 3-Dimensional Convolutional Neural Networks

Artificial Intelligence in Classical and Quantum Photonics

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