Novel Computer-Aided Diagnosis of Lung Disorders Using Deep Learning Approach

The COVID-19 virus brought huge changes all over the globe.Several works focused on classifying Lung disorders (Lung cancer, Pneumonia, COVID-19, and Healthy) images, but their proposed techniques did not achieve better accuracy.The main goal is to identify it at an early stage.The proposed work is to distinguish the types of lung disease, either in benign or malignant phase, and help to expert the radiologists in patient care.To classify lung disorder in stages, whether it is benign or malignant, a new technique known as ResVGG19 is employed for image analysis on CT scans of lungs.This finding comes with the help of Tensor Flow assistance in developing the simulation outcomes.Hence, obtain ResVGG19 classifier accuracy at 97.6% has surpassed both ResNet50 and U-Net.The experimental results verify that the suggested model has outperformed all other learning models, including ResNet50, U-Net, and VGG-Net.ResVGG19-Net produced results of 97.6% accuracy, 92% precision, 91% recall, and 91.3% F1-score when accuracy, precision, and recall were taken into account.The performance of ResVGG19-Net is superior to that of the state-of-the-art.