Related references
Note: Only part of the references are listed.Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss
Giang Son Tran et al.
JOURNAL OF HEALTHCARE ENGINEERING (2019)
Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features
Fursian Shaukat et al.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2019)
Cancer statistics, 2019
Rebecca L. Siegel et al.
CA-A CANCER JOURNAL FOR CLINICIANS (2019)
Hybrid-feature-guided lung nodule type classification on CT images
Jingjing Yuan et al.
COMPUTERS & GRAPHICS-UK (2018)
Survey of Computer Aided Detection Systems for Lung Cancer in Computed Tomography
Salsabil A. El-Regaily et al.
CURRENT MEDICAL IMAGING (2018)
Lung-Nodule Classification Based on Computed Tomography Using Taxonomic Diversity Indexes and an SVM
Antonio Oseas de Carvalho Filho et al.
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY (2017)
Lung nodule classification using artificial crawlers, directional texture and support vector machine
Bruno Rodrigues Froz et al.
EXPERT SYSTEMS WITH APPLICATIONS (2017)
Fully automatic detection of lung nodules in CT images using a hybrid featureset
Furqan Shaukat et al.
MEDICAL PHYSICS (2017)
Recommendations for Measuring Pulmonary Nodules at CT: A Statement from the Fleischner Society
Alexander A. Bankier et al.
RADIOLOGY (2017)
Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives
Senthilkumar Krishnamurthy et al.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE (2016)
Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images
Wei Li et al.
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2016)
A novel approach to CAD system for the detection of lung nodules in CT images
Muzzamil Javaid et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2016)
Automatic 3D pulmonary nodule detection in CT images: A survey
Igor Rafael S. Valente et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2016)
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
Hoo-Chang Shin et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)
A parameterized logarithmic image processing method with Laplacian of Gaussian filtering for lung nodule enhancement in chest radiographs
Sheng Chen et al.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2016)
Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique
Atsushi Teramoto et al.
MEDICAL PHYSICS (2016)
Pulmonary Nodules Detection and Classification Using Hybrid Features from Computerized Tomographic Images
Sheeraz Akram et al.
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS (2016)
Ensemble Tree Learning Techniques for Magnetic Resonance Image Analysis
Javier Ramirez et al.
INNOVATION IN MEDICINE AND HEALTHCARE 2015 (2016)
An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy
Shiwen Shen et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2015)
Proposing a classifier ensemble framework based on classifier selection and decision tree
Hamid Parvin et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2015)
Hybrid detection of lung nodules on CT scan images
Lin Lu et al.
MEDICAL PHYSICS (2015)
Automatic detection of large pulmonary solid nodules in thoracic CT images
Arnaud A. A. Setio et al.
MEDICAL PHYSICS (2015)
Artificial Neural Network based Classification of Lungs Nodule using Hybrid Features from Computerized Tomographic Images
Sheeraz Akram et al.
Applied Mathematics & Information Sciences (2014)
Artificial Neural Network based Classification of Lungs Nodule using Hybrid Features from Computerized Tomographic Images
Sheeraz Akram et al.
Applied Mathematics & Information Sciences (2014)
A relative evaluation of multiclass image classification by support vector machines
GM Foody et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2004)
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
TG Dietterich
MACHINE LEARNING (2000)