Classification of Lung Tissue with Cystic Fibrosis Lung Disease via Deep Convolutional Neural Networks

Quantitative classification of disease regions contained in lung tissues obtained from Computed Tomography (CT) scans is one of the key steps to evaluate lesion degrees of Cystic Fibrosis Lung Disease (CFLD). In this paper, we propose a deep Convolutional Neural Network-based (CNN) framework for automatic classification of lung tissues with CFLD. Core of the framework is the integration of deep CNNs into the classification workflow. To train and validate performance of deep CNNs, we build datasets for inspiration CT scans and expiration CT scans, respectively. We employ transfer learning techniques to fine tune parameters of deep CNNs. Specifically, we train Resnet-18 and Resnet-34 and validate the performance on the built datasets. Experimental results in terms of average precision and receiver operating characteristic curve demonstrate effectiveness of deep CNNs for classification of lung tissue with CFLD.