Lung Disease Detection and Classification with Deep Learning Approach
Araya Chatchaiwatkul, Pasuk Phonsuphee, Yurananatul Mangalmurti, Naruemon Wattanapongsakorn
Abstract
Nowadays, COVID-19 outbreak and respiratory symptoms globally take a huge number of people's lives away. Especially, COVID-19, which is a pandemic initially spreading out in the first quarter of the year 2020, heavily affects many people to die. Most countries have tried to find ways to solve and mitigate this outbreak including respiratory diseases due to the mentioned reason. We also face with insufficient number of medical personnel and equipment to treat the diseases. The need of technology to analyze the images for the disease detection is quite a challenge. In this work, we consider detecting and classifying many lung diseases from chest X-ray images using a deep learning (artificial intelligence) approach with VGG16 models. The lung diseases are COVID-19, Pneumonia and Pneumothorax. We use quite large published disease datasets. Our detection and classification models give impressive results providing between 93% and 100% accuracy, precision, recall and F1-measure.