Pneumonia binary classification using multi-scale feature classification network on chest x-ray images

Abstract

According to the world health organization, pneumonia was the cause for 14% of all deaths of children under 5 years old. A computer-aided diagnosis (CADx) system can help the radiologist in the detection of pneumonia in chest radiographs by serving as a second opinion. The typical CADx is based on transfer learning which is done by transferring the learning of feature extraction from one task with plenty of available data to a related task with a scarcity of data. This approach has two limitations which are first, blocking the transferred model from extracting the features that are singular to the new dataset as well as the inability to reduce the complexity of the original model. To address these drawbacks, we proposed a convolutional neural network (CNN) model with low complexity and three paths for feature extraction. The proposed model extracts three different types of features and concatenates them into one feature that provides a good representation for the classes. The proposed model was evaluated on a publicly available dataset. The results showed outperformance by the proposed model compared to the transfer learning models with recall 0.912±0.039, precision 0.942±0.029, F-beta score 0.93, and Cohen’s kappa score 0.740±0.008.