Lung Nodule Classification Using Deep Features in CT Images

By Devinder Kumar, Alexander Wong, David A Clausi. 2015 12th Conference on Computer and Robot Vision, 2015

330 citations recorded by the source corpus.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9a69b2d7e15e0ebd66d4aaef1bc91b06bed7bf65

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In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep learning for lung Cancer detection and classification (2020, methodology): “For lung cancer screening, Nowadays Low-Dose helical Computed Tomography (LDCT) [18] is being applied as a modality [31].”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, neutral): “[45] CAD classifier with deep features from autoencoder CT Lung nodule classification; this approach resulted an accuracy of 75.”
Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN) (2017, methodology): “Table 1: typical radiodensities in HU of various substances in a CT scan [8]”
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT (2019, methodology): “[57] used auto-encoders and CNNs to classify lung nodules as malignant or benign, with an accuracy of 77.”
Deep learning in radiology: an overview of the concepts and a survey of the state of the art with focus on MRI (2018, methodology): “Such feature representation has been used in the classification of lung nodules into benign and malignant classes in CT [48], and in the identification of multiple sclerosis lesions in MRI [49].”
Deep Learning in Medical Imaging: General Overview (2017, background): “Another important limitation of the current CAD systems is susceptibility to Early reports of deep learning based CAD systems for breast cancer (86), lung cancer (87, 88) and Alzheimer’s disease (AD) (89-91) show promising results regarding their performance in detecting and staging the diseases.”
Deep Feature Learning for Medical Image Analysis with Convolutional Autoencoder Neural Network (2017, methodology): “We compare the classification performance of (CANN), autoencoder (AE) [30], convolutional neural network (CNN) and MCCNN [22] with dataset D2, the results are shown in Table 1 and the Rate of Change (ROC) is shown in Fig.”
AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, positive): “and then employing a decision tree for distinguishing between benign nodules and malignant ones was proposed in [129].”
Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging (2019, methodology): “The study by Kumar and colleagues depended on manual delineation of lung nodules with feature extraction using an autoencoder and classification with decision trees (46).”