COVID-19 Classification Using Staked Ensembles: A Comprehensive Analysis

TL;DR

This paper evaluates various vision models and their stacked ensemble for rapid, accurate COVID-19 diagnosis using chest imaging, achieving a state-of-the-art accuracy of 99.17% with high robustness.

Contribution

It introduces a comprehensive analysis of variant vision models, visual interpretability via CAMs, and the development of stacked ensemble techniques for improved COVID-19 classification.

Findings

Stacked ensembles outperform individual models in accuracy.

Achieved 99.17% accuracy with ensemble models.

High precision (99.99%) and recall (89.79%) for COVID-19 detection.

Abstract

The issue of COVID-19, increasing with a massive mortality rate. This led to the WHO declaring it as a pandemic. In this situation, it is crucial to perform efficient and fast diagnosis. The reverse transcript polymerase chain reaction (RTPCR) test is conducted to detect the presence of SARS-CoV-2. This test is time-consuming and instead chest CT (or Chest X-ray) can be used for a fast and accurate diagnosis. Automated diagnosis is considered to be important as it reduces human effort and provides accurate and low-cost tests. The contributions of our research are three-fold. First, it is aimed to analyse the behaviour and performance of variant vision models ranging from Inception to NAS networks with the appropriate fine-tuning procedure. Second, the behaviour of these models is visually analysed by plotting CAMs for individual networks and determining classification performance with AUCROC curves. Thirdly, stacked ensembles techniques are imparted to provide higher generalisation on combining the fine-tuned models, in which six ensemble neural networks are designed by combining the existing fine-tuned networks. Implying these stacked ensembles provides a great generalization to the models. The ensemble model designed by combining all the fine-tuned networks obtained a state-of-the-art accuracy score of 99.17%. The precision and recall for the COVID-19 class are 99.99% and 89.79% respectively, which resembles the robustness of the stacked ensembles.