Ensemble Synergy: A Novel Approach to COVID-19 Detection

Abstract

COVID-19 pandemic has necessary fast, correct, and scalable diagnostic methods. In this research, EvoNet is introduced asan modern ensemble version that combines the strengths of three deep learning architectures: VGG16, ResNet50, and InceptionV3.  EvoNet is carefully made better by a lot.This work addresses the constraints of widespread COVID-19 tests, which may be time-consuming and susceptible to fake negatives.From the combination of  transfer learning and data augmentation , thismethod affords a distinctly correct answer for COVID-19 detection from X-ray images. Furthermore, we explore the fusion of those models, visualizing a collaborative approach which could yield even more strong effects. In a time when rapid, dependable, and scalable diagnostic tools are essential, EvoNet represents a promising advancement in the combat against COVID-19, supplying avital complement to conventional testing methods.