Fighting together against the pandemic: learning multiple models on tomography images for COVID-19 diagnosis

TL;DR

This paper proposes an ensemble deep learning approach using pretrained convolutional neural networks to improve COVID-19 diagnosis from CT images, demonstrating superior performance over existing methods.

Contribution

It introduces a novel ensemble framework combining multiple pretrained neural architectures for COVID-19 detection on CT images, enhancing diagnostic accuracy.

Findings

Ensemble approach outperforms individual models in accuracy.

Pretrained neural networks effectively adapted for medical image analysis.

Experimental results surpass state-of-the-art benchmarks.

Abstract

The great challenge for the humanity of the year 2020 is the fight against COVID-19. The whole world is making a huge effort to find an effective vaccine with purpose to protect people not yet infected. The alternative solution remains early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) test or thorax computer tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis. They optimize the classification design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopt pretrained deep convolutional neural network architectures in order to diagnose COVID-19 disease on CT images. Our idea is inspired by what the whole of humanity is achieving, substantially the set of multiple contributions is better than the single one for the fight against the pandemic. Firstly, we adapt, and subsequently retrain, for our assumption some neural architectures adopted in other application domains. Secondly, we combine the knowledge extracted from images by neural architectures in an ensemble classification context. Experimental phase is performed on CT images dataset and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.