Optimizing Convolutional Neural Networks for Chronic Obstructive Pulmonary Disease Detection in Clinical Computed Tomography Imaging

Zielsetzung Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death worldwide, yet early detection and treatment can prevent the progression disease. X-ray Computed Tomography (CT) scans chest provide quantitative measure morphological changes in lung. The potential incorporating optimal Window Setting (WS) selection, typically carried out during examination CT for COPD, generally overlooked deep learning models. We aim to optimize COPD with densely connected convolutional neural networks (DenseNets) through implementation Window-Setting Optimization (WSO) steps.