Computer-Aided Lung Cancer Diagnosis Approaches Based on Deep Learning

2018 ◽  
Vol 30 (1) ◽  
pp. 90 ◽  
Author(s):  
Peng Zhang ◽  
Xinnan Xu ◽  
Hongwei Wang ◽  
Yuanli Feng ◽  
Haozhe Feng ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Lung Cancer Diagnosis ◽  
Computer Aided

Early-Stage Lung Cancer Diagnosis by Deep Learning-Based Spectroscopic Analysis of Circulating Exosomes

ACS Nano ◽  
2020 ◽  
Vol 14 (5) ◽  
pp. 5435-5444 ◽  
Author(s):  
Hyunku Shin ◽  
Seunghyun Oh ◽  
Soonwoo Hong ◽  
Minsung Kang ◽  
Daehyeon Kang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Spectroscopic Analysis ◽  
Early Stage ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Diagnosis ◽  
Stage Lung Cancer

scholarly journals Cloud-Based NoSQL Open Database of Pulmonary Nodules for Computer-Aided Lung Cancer Diagnosis and Reproducible Research

2016 ◽  
Vol 29 (6) ◽  
pp. 716-729 ◽  
Author(s):  
José Raniery Ferreira Junior ◽  
Marcelo Costa Oliveira ◽  
Paulo Mazzoncini de Azevedo-Marques
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Pulmonary Nodules ◽  
Reproducible Research ◽  
Lung Cancer Diagnosis ◽  
Computer Aided

Deep learning application trial to lung cancer diagnosis for medical sensor systems

2016 ◽  
Author(s):  
Ryota Shimizu ◽  
Shusuke Yanagawa ◽  
Yasutaka Monde ◽  
Hiroki Yamagishi ◽  
Mototsugu Hamada ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Diagnosis ◽  
Sensor Systems ◽  
Lung Cancer Diagnosis

scholarly journals Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

2013 ◽  
Vol 2013 ◽  
pp. 1-46 ◽  
Author(s):  
Ayman El-Baz ◽  
Garth M. Beache ◽  
Georgy Gimel'farb ◽  
Kenji Suzuki ◽  
Kazunori Okada ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Clinical Importance ◽  
Computer Aided Diagnosis ◽  
Cad Systems ◽  
Cad System ◽  
Lung Cancer Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Processing Steps

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effectivecomputer-aided diagnosis(CAD) system for lung cancer is of great clinical importance and can increase the patient’s chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems.

Development of a computer-aided detection system for lung cancer diagnosis

1992 ◽  
Author(s):  
Hideo Suzuki ◽  
Noriko Inaoka ◽  
Hirotsugu Takabatake ◽  
Masaki Mori ◽  
Soichi Sasaoka ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Detection System ◽  
Computer Aided Detection ◽  
Lung Cancer Diagnosis ◽  
Computer Aided
Sign in / Sign up

Export Citation Format

Share Document