Recent advancements in deep learning based lung cancer detection: A systematic review

S Dodia, B Annappa, PA Mahesh - Engineering Applications of Artificial …, 2022 - Elsevier
Cancer is considered to be a key cause of substantial fatality and morbidity in the world. A
report from the International Agency for Research on Cancer (IARC) states that 27 million …

Automatic 3D pulmonary nodule detection in CT images: a survey

IRS Valente, PC Cortez, EC Neto, JM Soares… - Computer methods and …, 2016 - Elsevier
This work presents a systematic review of techniques for the 3D automatic detection of
pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze …

Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge

AAA Setio, A Traverso, T De Bel, MSN Berens… - Medical image …, 2017 - Elsevier
Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has
been an active area of research for the last two decades. However, there have only been …

Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks

AAA Setio, F Ciompi, G Litjens, P Gerke… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using
multi-view convolutional networks (ConvNets), for which discriminative features are …

Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection

Q Dou, H Chen, L Yu, J Qin… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Objective: False positive reduction is one of the most crucial components in an automated
pulmonary nodule detection system, which plays an important role in lung cancer diagnosis …

Improving computer-aided detection using convolutional neural networks and random view aggregation

HR Roth, L Lu, J Liu, J Yao, A Seff… - IEEE transactions on …, 2015 - ieeexplore.ieee.org
Automated computer-aided detection (CADe) has been an important tool in clinical practice
and research. State-of-the-art methods often show high sensitivities at the cost of high false …

[PDF][PDF] Lung cancer detection and classification with 3D convolutional neural network (3D-CNN)

W Alakwaa, M Nassef, A Badr - International Journal of Advanced …, 2017 - academia.edu
This paper demonstrates a computer-aided diagnosis (CAD) system for lung cancer
classification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science …

Lung nodule detection based on faster R-CNN framework

Y Su, D Li, X Chen - Computer Methods and Programs in Biomedicine, 2021 - Elsevier
Background Lung cancer is a worldwide high-risk disease, and lung nodules are the main
manifestation of early lung cancer. Automatic detection of lung nodules reduces the …

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 …

Multi-view multi-scale CNNs for lung nodule type classification from CT images

X Liu, F Hou, H Qin, A Hao - Pattern Recognition, 2018 - Elsevier
In this paper, we propose a novel convolution neural networks (CNNs) based method for
nodule type classification. Compared with classical approaches that are handling four solid …