Lung nodule detection from feature engineering to deep learning in thoracic CT images: a comprehensive review
This paper presents a systematic review of the literature focused on the lung nodule
detection in chest computed tomography (CT) images. Manual detection of lung nodules by …
detection in chest computed tomography (CT) images. Manual detection of lung nodules by …
Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: a systematic review
LM Pehrson, MB Nielsen, C Ammitzbøl Lauridsen - Diagnostics, 2019 - mdpi.com
The aim of this study was to provide an overview of the literature available on machine
learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection …
learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection …
An automatic detection system of lung nodule based on multigroup patch-based deep learning network
High-efficiency lung nodule detection dramatically contributes to the risk assessment of lung
cancer. It is a significant and challenging task to quickly locate the exact positions of lung …
cancer. It is a significant and challenging task to quickly locate the exact positions of lung …
Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy
M Firmino, G Angelo, H Morais, MR Dantas… - Biomedical engineering …, 2016 - Springer
Abstract Background CADe and CADx systems for the detection and diagnosis of lung
cancer have been important areas of research in recent decades. However, these areas are …
cancer have been important areas of research in recent decades. However, these areas are …
Convolutional neural network-based PSO for lung nodule false positive reduction on CT images
GLF Da Silva, TLA Valente, AC Silva… - Computer methods and …, 2018 - Elsevier
Background and objective Detection of lung nodules is critical in CAD systems; this is
because of their similar contrast with other structures and low density, which result in the …
because of their similar contrast with other structures and low density, which result in the …
Hierarchical voting-based feature selection and ensemble learning model scheme for glioma grading with clinical and molecular characteristics
Determining the aggressiveness of gliomas, termed grading, is a critical step toward
treatment optimization to increase the survival rate and decrease treatment toxicity for …
treatment optimization to increase the survival rate and decrease treatment toxicity for …
Rapid assessment of acute ischemic stroke by computed tomography using deep convolutional neural networks
Acute stroke is one of the leading causes of disability and death worldwide. Regarding
clinical diagnoses, a rapid and accurate procedure is necessary for patients suffering from …
clinical diagnoses, a rapid and accurate procedure is necessary for patients suffering from …
Multi-view convolutional recurrent neural networks for lung cancer nodule identification
Screening via low-dose Computer Tomography (CT) has been shown to reduce lung cancer
mortality rates by at least 20%. However, the assessment of large numbers of CT scans by …
mortality rates by at least 20%. However, the assessment of large numbers of CT scans by …
Computer-aided diagnosis: A survey with bibliometric analysis
R Takahashi, Y Kajikawa - International journal of medical informatics, 2017 - Elsevier
Computer-aided diagnosis (CAD) has been a promising area of research over the last two
decades. However, CAD is a very complicated subject because it involves a number of …
decades. However, CAD is a very complicated subject because it involves a number of …
Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database …
MC Hancock, JF Magnan - Journal of Medical Imaging, 2016 - spiedigitallibrary.org
In the assessment of nodules in CT scans of the lungs, a number of image-derived features
are diagnostically relevant. Currently, many of these features are defined only qualitatively …
are diagnostically relevant. Currently, many of these features are defined only qualitatively …