Deep Learning Fusion for COVID-19 Diagnosis

Abstract

The outbreak of the novel coronavirus (COVID-19) disease has spurred a tremendous research boost aiming at controlling it. Under this scope, deep learning techniques have received even more attention as an asset to automatically detect patients infected by COVID-19 and reduce the doctor’s burden to manually assess medical imagery. Thus, this work considers a deep learning architecture that fuses the layers of current-state-of-the-art deep networks to produce a new structure-fused deep network. The advantages of our deep network fusion scheme are multifold, and ultimately afford an appealing COVID-19 automatic diagnosis that outbalances current deep learning methods. Indeed, evaluation on Computer Tomography (CT) and X-ray imagery considering a two-class (COVID-19/ non-COVID-19) and a four-class (COVID-19/ non-COVID-19/ Pneumonia bacterial / Pneumonia virus) classification problem, highlights the classification capabilities of our method attaining 99.3% and 100%, respectively.

A Survey on Machine Learning in COVID-19 Diagnosis

“…The results showed that the classification accuracy of this model was 99.08% in the multi-classification task and the classification accuracy of 99.53% in the binary classification task. Kechagias-Stamatis et al [121] proposed a new structure-fusion deep learning network, which integrated the layers of GoogleNet and ResNet18. In addition, they evaluated the model in two experiments, binary classification, and multi-classification.…”

“…The accuracy of the proposed network was 99.08% in the multi-classification task and 99.53% in the binary classification task. Kechagias-Stamatis et al [121] X-ray, CT GoogleNet, ResNet18, a structure-fusion deep learning network The classification capability of the structure-fusion deep learning network was evaluated on CT and X-ray datasets with 99.30% and 100.00% classification accuracy, respectively.…”

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19

“…The classification of COVID-19 and pneumonia seeks to separate patients infected with SARS-CoV-2 from those with pneumonia caused by another agent [7,[42][43][44][45][46][47][48][49][50]. The classification of COVID-19 and non-COVID-19 seeks to distinguish patients diagnosed with infection caused by SARS-Cov-2, from those that present characteristics of classic pneumonia, healthy or with a diagnostic of other lung diseases [51][52][53][54][55][56][57][58][59][60]. In addition, some works presented different methodologies [7] that are able to detect the lesion of the image indicating its severity.…”