SoftLungX: leveraging transfer learning with convolutional neural networks for accurate respiratory disease classification in chest X-ray images

Medical imaging is an indispensable and very important step in the diagnosis and treatment of illnesses. However, due to large amounts of resources necessary for training the model, training from scratch may not invariably emerge as the optimal recourse. Transfer learning has emerged as a viable solution, where pretrained weights from ImageNet are initially utilized and fine-tuninned afterwards. This paper presents a novel approach employing transfer learning to classify both respiratory diseases and radiological findings from chest X-rays. The dataset comprises 191 660 X-ray images gathered from public databases and 752 chest X-ray images obtained from a retrospective study at the University Clinical Center of Kragujevac, forming the foundation of Big data analysis. It includes various conditions such as atelectasis, cardiomegaly, infiltration, pleural thickening, non-viral pneumonia, pneumothorax, COVID-19 viral pneumonia, tuberculosis, etc. as well as masses and nodules indicative of tumors and images of healthy subjects. Although there are existing solutions with some diseases, this is the first paper that inlcludes differentiation between healthy and diseased cases, as well as in case of present disease, as much as 18 different classes are inlcuded in the analysis, which has not been done so far. The proposed methodology included transfer learning with DenseNet121 as the backbone and CheXNeXt weights as initialisation scheme, upon which additional layers were added for fine tuning. The results demonstrate the model’s capacity to distinguish between healthy and diseased, with an average area under curve (AUC) of 0.99. The AUC ranges from 0.86 for nodules to 0.99 for pneumonia and 0.99 for COVID-19. Our method outperforms the state-of-the-art in accuracy across all 18 classes, with the exception of classes consolidation, mass, and nodule. These findings are promising for a tested clinical site, though further validation in clinical environment accros multiple sites may be necessary before using the model as a standalone decision system, rather than a decision support system for disease diagnosis based on chest X-rays.