Detecting lung diseases from X-Ray images using deep learning

Abstract

Lung disease has become one of the most dangerous diseases worldwide after the Covid-19 pandemic. Early diagnosis of lung disease is vital for effective treatment and recovery. In clinical practice, X-ray imaging is currently the most widely used method for diagnosis, and it plays a crucial role as a life-saving factor for individuals suffering from the disease. In recent years, many deep learning approaches have been proposed for the early diagnosis of lung diseases from X-ray images. These approaches have shown high accuracy in predicting the results within a short time. This paper aims to compare different state-of-the-art deep learning models for the task of lung-disease diagnosis. Additionally, we have collected a new dataset of lung disease X-ray images from hospitals in Vietnam to evaluate the performance of each model based on validation loss and validation accuracy. The results show that our proposed deep learning model achieves an accuracy of 98.35% (training) and 86.65% (validation) on the new ChestVN lung disease dataset, which promises to be a good method for applying in daily life. The proposed approach has the potential to assist medical professionals in the early diagnosis of lung diseases, which can lead to better patient outcomes and improved healthcare management.