Prediction of Covid-19 From Lung CT Images Using Fine-Tuned Deep Learning Models

Abstract

Objectives: This research article's principal objective is to improve and create a fully automated methodology for the recognition and diagnosis of infections in the lungs Computed Tomography pictures using pre-trained neural network architectures. Methods: The proposed model have employed a total 746 CT scan sample which is openly accessible, comprising 349 scans who tested positive for COVID-19 and 397 scans that are either healthy or show different respiratory illnesses. Convolutional neural networks (CNNs) with four different architectures—InceptionV3, ResNet-50, Xception, and VGG19 are employed and refined based on the intended objective. After then, in order to increase performance metrics, an ensemble technique employing the Adam optimizer have developed. Findings: These outcomes demonstrated the utility of Artificial intelligence in identifying COVID-19 instances. When measured against every other deep learning model the N-VGG-19 revealed an accuracy of 90%, indicating its better performance. Novelty: Using the Adamax optimizer and data augmentation approach, The proposed method have contributed a thorough assessment of many already trained neural network modules such as ResNet50, InceptionV3, N-VGG19, and Xception. Keywords: Artificial intelligence (AI), COVID-19, Convolutional Neural Network, CT Scan, Deep Learning