Automated lung diseases analysis using deep learning techniques
Chronic obstructive pulmonary disease (COPD) is a respiratory disorder that is mainly caused by exogenous factors like tobacco smoking and air pollution. COPD is compose d of two main components: emphysema (airspace enlargement and tissue destruction) and bronchitis (airways disease). Low-dose computed tomography (LDCT) is an effective modality for early detection of COPD mainly emphysema. Hence in this study, our aim is to consider all the clinically quantifiable features and image features to develop a prognosis and classification model for COPD using NLST data.
1. To develop models based on deep learning techniques for classification of lung cancer
2. To validate our own models for lung nodule detection and classification based on the NLST dataset
3. To develop and compare different deep learning based classification model for COPD
4. To validate the deep learning model using NSCT for reproduciability
Sunyi Zheng, University Medical Center Groningen, firstname.lastname@example.org
Yeshaswini Nagaraj, University Medical Center Groningen, email@example.com