Pneumonia Detection with Chest-Caps

Abstract

Pneumonia is one of the diseases with the highest mortality in children. Early diagnosis is vital for the recovery of children and saving their lives. With the developments in artificial intelligence, the use of computer aided systems has become widespread. This has increased reliable, accurate and fast on studies about classification, segmentation and detection. In this study, pneumonia and healthy chest X-ray images were classified using capsule network. This model is specialized and adapted to the study in a specific way. K-fold cross validation and preprocessing of images were also applied to improve the study performance. As a result of the study, accuracy, precision, recall, F1-score and AUC scores were obtained as 0.984, 0.996, 0.971, 0.983, 0.974, respectively. The proposed model has been compared with state-of-the-art models and studies in the literature, and it is seen that our study has achieved excellent results.