查看文章

Cystic fibrosis (CF) is one of the most prevalent autosomal recessive disorders among whites. It mostly affects lungs which causes infection and inflammation leading to 90% of deaths among CF patients. Due to wide variability in its clinical presentation and organ involvement, studying responses to therapy and evaluation of pulmonary changes over time is crucial in progress prevention of CF. Serial high-resolution computed tomography (HRCT) scans significantly facilitate the assessment of the pulmonary abnormalities evolution in CF patients. Recently, artificial intelligence is being employed for analyzing thoracic CT scans acquired from CF patients. In this paper, we propose a convolutional neural network (CNN) approach for classifying CF lung patterns in HRCT images. The proposed network consists of two convolutional layers with 3 × 3 kernels and max-pooling in each layer followed by two dense …