Deep Transfer Learning with Apache Spark to Detect COVID-19 in chest X-ray Images

Houssam Benbrahim, Hanaâ Hachimi, Aouatif Amine

2020Romanian Journal of Information Science and Technology32 citations

Abstract

A chest X-ray test is one of the most important and recurrent medical imaging examinations It is the first imaging technique that represents a significant role in the diagnosis of SARS-CoV-2 disease Automatic classification of 2019-nCoV using X-ray images is a major request that can help doctors to make the best decisions In this paper, we adopted, developed, and validated a Deep Transfer Learning (DTL) method using Convolutional Neural Network (CNN) based models InceptionV3 and ResNet50 with Apache Spark framework for the classification of COVID-19 in chest X-ray images collected from Kaggle repository High accuracy was obtained by our model in the detection of COVID-19 X-ray images 99 01% by the pre-trained InceptionV3 model and 98 03% for the ResNet50 model

Topics & Concepts

Transfer of learningConvolutional neural networkCoronavirus disease 2019 (COVID-19)Deep learningSPARK (programming language)Computer scienceArtificial intelligenceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakPattern recognition (psychology)MedicinePathologyDiseaseProgramming languageInfectious disease (medical specialty)OutbreakCOVID-19 diagnosis using AIRadiomics and Machine Learning in Medical ImagingAI in cancer detection