Computer‐aided diagnosis systems for lung cancer: challenges and methodologies

A El-Baz, GM Beache, G Gimel′ farb… - … journal of biomedical …, 2013 - Wiley Online Library
This paper overviews one of the most important, interesting, and challenging problems in
oncology, the problem of lung cancer diagnosis. Develo** an effective computer-aided …

Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification

W Zhu, C Liu, W Fan, X **e - 2018 IEEE winter conference on …, 2018 - ieeexplore.ieee.org
In this work, we present a fully automated lung computed tomography (CT) cancer diagnosis
system, DeepLung. DeepLung consists of two components, nodule detection (identifying the …

Multi-scale convolutional neural networks for lung nodule classification

W Shen, M Zhou, F Yang, C Yang, J Tian - … 2015, Sabhal Mor Ostaig, Isle of …, 2015 - Springer
We investigate the problem of diagnostic lung nodule classification using thoracic Computed
Tomography (CT) screening. Unlike traditional studies primarily relying on nodule …

Alzheimer's disease diagnostics by adaptation of 3D convolutional network

E Hosseini-Asl, R Keynton… - 2016 IEEE international …, 2016 - ieeexplore.ieee.org
Early diagnosis, playing an important role in preventing progress and treating the
Alzheimer's disease (AD), is based on classification of features extracted from brain images …

SANet: A slice-aware network for pulmonary nodule detection

J Mei, MM Cheng, G Xu, LR Wan… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Lung cancer is the most common cause of cancer death worldwide. A timely diagnosis of the
pulmonary nodules makes it possible to detect lung cancer in the early stage, and thoracic …

Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches

S Hussein, P Kandel, CW Bolan… - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Risk stratification (characterization) of tumors from radiology images can be more accurate
and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such …

Texture feature analysis for computer-aided diagnosis on pulmonary nodules

F Han, H Wang, G Zhang, H Han, B Song, L Li… - Journal of digital …, 2015 - Springer
Differentiation of malignant and benign pulmonary nodules is of paramount clinical
importance. Texture features of pulmonary nodules in CT images reflect a powerful …

An appraisal of lung nodules automatic classification algorithms for CT images

X Wang, K Mao, L Wang, P Yang, D Lu, P He - Sensors, 2019 - mdpi.com
Lung cancer is one of the most deadly diseases around the world representing about 26% of
all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent …

Risk stratification of lung nodules using 3D CNN-based multi-task learning

S Hussein, K Cao, Q Song, U Bagci - … , IPMI 2017, Boone, NC, USA, June …, 2017 - Springer
Risk stratification of lung nodules is a task of primary importance in lung cancer diagnosis.
Any improvement in robust and accurate nodule characterization can assist in identifying …

On the performance of lung nodule detection, segmentation and classification

D Gu, G Liu, Z Xue - Computerized Medical Imaging and Graphics, 2021 - Elsevier
Computed tomography (CT) screening is an effective way for early detection of lung cancer
in order to improve the survival rate of such a deadly disease. For more than two decades …