COVIDEffiNet: Pulmonary Diseases and COVID-19 Detection from Chest Radiographs Using EfficientNet Deep Learning Model

There have been attempts made previously to classify and determine the diagnosis of a disease of a patient based on the X-rays and computed tomography images of various parts of the body. In the field of lung disease diagnosis, there have been attempts to identify lungs infected with pneumonia, COVID-19, and tuberculosis, either individually classifying them into two groups of positive and negative of the given disease or in groups with multiple classes. These methods and approaches have used various deep learning models like CNNs, ResNet50, VGG19, Inception V3, MobileNet_V2, hybrid models, and ensemble learning methods. In this paper, we have proposed a model that takes an X-ray image of the lungs of the patients as input and classifies the result as one of the following classes: tuberculosis, pneumonia, COVID-19, or normal, that is, healthy lungs. What we have used here is transfer learning, with our base model being EfficientNet which gives an accuracy of 93%. For this, we have used different datasets of X-ray images of patients with different lung ailments, namely pneumonia, tuberculosis, and COVID. The dataset consists of images in four categories, the above-mentioned three diseases and a fourth category of normal healthy lungs.