Augmented Intelligence Based COVID-19 Diagnostics and Deep Feature Categorization Based on Federated Learning

Abstract

The global pandemic of COVID-19 has had profound and devastating effects on human life since its emergence in 2019. This viral infection predominantly impacts the respiratory system, causing a range of severity in alveolar overlapping that results in breathlessness and fatality. A novel methodology was assessed using the primary COVID-19 dataset from Kaggle, employing a federated learning ecosystem with multi-user datasets. This technique involves extracting data logs from various user repositories and datasets within the federated learning framework. Subsequently, a validation process is conducted, followed by computation utilizing a deep feature set categorization technique augmented by artificial intelligence. This augmented intelligence is showcased in a multi-layer image classification system designed for feature recognition and extraction. The training dataset, comprising 1056 data samples, is split into 647 for training and 409 for testing. Experimental outcomes highlighted a more comprehensive mapping and prioritization of features relative to attribute values. Remarkably, the proposed classification technique surpasses existing methods in accurately labeling COVID-19 detection as opposed to pneumonia and normal lung conditions in MRI/CT images.