Transfer Learning with Deep Convolutional Neural Networks for Respiratory Disease Classification in X-Ray Images

Abstract

Medical imaging plays an important role in medicine today, assisting in illness diagnosis and therapy. For limited medical image datasets, training from scratch is not an option, hence transfer learning emerges as a solution, with ImageNet weights being utilised as initial weights, followed by fine-tuning. This paper takes a different approach by introducing transfer learning approach with pretrained architecture DenseNet121 with CheXNeXt weights. Collected dataset consisted of 227269 X-Ray images from public databases and 684 chest X-Ray images from a retrospective study conducted in the University Clinical Center of Kragujevac and included information on atelectasis, cardiomegaly, parenchymal consolidation, edema, effusion, emphysema, fibrosis, hiatus hernia, infiltration, pleural thickening, non-viral pneumonia, pneumothorax, viral pneumonia in the form of Covid-19, tuberculosis as well as tumors in the form of mass and nodules. The results show that the model is able to distinguish between the healthy and diseased lungs with average AUC of 0.91 (the lowest AUC of 0.8 for emphysema and the highest AUC for of 0.99 for pneumonia and 0.98 for COVID-19). Although the results seem promising, additional fine tuning may be necessary to improve other metrics. Future research will focus on this aspect, as well as on creating a glass box system for classification.