Comparison of EfficientNet CNN models for multi-label chest X-ray disease diagnosis

Abstract

The analysis of chest X-ray images, which are critical for the early diagnosis of many diseases, is a difficult and time-consuming process due to the multiple labeling requirements and similar looking pathologies. In traditional methods, expert physicians analyze high-resolution chest X-ray images to diagnose these diseases using observational methods, a process that can lead to human error and hence misdiagnosis or underdiagnosis. In this study, we aim to autonomously detect 14 different diseases that significantly affect human health and some cases even lead to death using chest X-ray images in a multi-class manner using deep learning techniques. Previous studies on chest X-ray images focus on a single disease or have low success rates, and the architectures presented in previous studies have high computational costs. The novelty of this work is that it presents a hybrid lightweight, fast and attention-based architecture with high classification performance. In this study, we used the ChestX-Ray14 dataset consisting of 112,104 labeled chest X-ray images of 14 disease classes. Eight deep learning architectures (EfficientNetB0-B7) and coordinate attention mechanism are used in the training and testing processes. The proposed EfficientNetB7 architecture achieved an average overall classification performance with an AUC value of 0.8265. The EfficientNet enhanced with coordinate attention architecture achieved a classification success with an AUC value of 0.8309. Moreover, when the proposed architecture and the individual disease classes are considered separately, higher classification success is achieved for eight of the 14 diseases in the dataset. Finally, the results of this study outperformed the classification performance of other similar studies in the literature in terms of AUC score. The results obtained in our study show that the proposed deep learning based lightweight and fast architecture can support radiologists in decision making in disease diagnosis. The use of autonomous disease diagnosis systems can support the protection of human health by preventing incomplete or erroneous diagnoses.