RED-CNN: The Multi-Classification Network for Pulmonary Diseases

Abstract

Deep learning is a convenient method for doctors to classify pulmonary diseases such as COVID-19, viral pneumonia, bacterial pneumonia, and tuberculosis. However, such a task requires a dataset including samples of all these diseases and a more effective network to capture the features of images accurately. In this paper, we propose a five-classification pulmonary disease model, including the pre-processing of input data, feature extraction, and classifier. The main points of this model are as follows. Firstly, we present a new network named RED-CNN which is based on CNN architecture and constructed using the RED block. The RED block is composed of the Res2Net module, ECA module, and Double BlazeBlock module, which are capable of extracting more detailed information, providing cross-channel information, and enhancing the extraction of global information with strong feature extraction capability. Secondly, by merging two selected datasets, the Curated Chest X-Ray Image Dataset for COVID-19 and the tuberculosis (TB) chest X-ray database, we constructed a new dataset including five types of data: normal, COVID-19, viral pneumonia, bacterial pneumonia, and tuberculosis. In order to assess the efficiency of the proposed five-classification model, a series of experiments based on the new dataset were carried out and based on 5-fold cross validation, and the results of the accuracy, precision, recall, F1 value, and Jaccard scores of the proposed method were 91.796%, 92.062%, 91.796%, 91.892%, and 86.176%, respectively. Our proposed algorithm performs better than other classification algorithms.