DEEP LEARNING - POWERED DIAGNOSIS OF PULMONARY DISEASES VIA X-RAY IMAGING

Abstract

Today, machine learning and deep learning have had many positive results in helping to diagnose and treat diseases. Based on data, parameters, and images such as X-ray, ultrasound, and magnetic resonance imaging, machines can help doctors diagnose and treat diseases better. This paper presents initial experiments on using deep learning to identify pulmonary diseases through X-ray image recognition. In experiments, there were three pulmonary diseases: aortic enlargement, lung opacity, and another lesion. There were also cases without disease to identify. The deep learning model with convolution neural network and DenseNet121 were used for our experiments with X-ray image data from Vietnamese samples and provided by VinBigData. The highest average identification accuracy achieved for pleural thickening and pulmonary fibrosis was 91.68% using DenseNet121.