COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities

COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities

Blog Article

The outbreak of COVID-19 has caused more than 200,000 deaths so far in the USA alone, which instigates the necessity of initial screening to control the spread of the onset of COVID-19.However, screening for the disease becomes laborious with the available testing kits as the number of patients increases rapidly.Therefore, to reduce the dependency on the limited test kits, many studies suggested a computed tomography (CT) scan or chest radiograph (X-ray) based screening system as an alternative approach.Thereby, to reinforce these approaches, models using both CT scan and chest X-ray images need to develop to conduct a large number of tests Ice Bucket simultaneously to detect patients with COVID-19 symptoms.In this work, patients with COVID-19 symptoms have been detected using eight distinct deep learning techniques, which are VGG16, InceptionResNetV2, ResNet50, DenseNet201, VGG19, MobilenetV2, NasNetMobile, and ResNet15V2, using two datasets: one dataset includes 400 CT scan and another 400 chest Scales and Body Composition Analyzers - Scales X-ray images.

Results show that NasNetMobile outperformed all other models by achieving an accuracy of 82.94% in CT scan and 93.94% in chest X-ray datasets.Besides, Local Interpretable Model-agnostic Explanations (LIME) is used.Results demonstrate that the proposed models can identify the infectious regions and top features; ultimately, it provides a potential opportunity to distinguish between COVID-19 patients with others.