Mentoring within the medical radiation sciences – Establishing a national program.

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Abstract

The aim of this study was to compare the accuracy and performance of 12 pre-trained deep learning models for classifying covid-19 and normal chest X-ray images from Kaggle.a desktop computer with an Intel CPU i9-10900 2.80GHz and NVIDIA GPU GeForce RTX2070 SUPER, Anaconda3 software with 12 pre-trained models including VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, RestNet50V2, RestNet101V2, RestNet152V2, InceptionRestnetV2, InceptionV3, XceptionV1 and MobileNetV2, covid-19 and normal chest X-ray from Kaggle website.the images were divided into three sets of train, test, and validation sets using a ratio of 70:20:10, respectively. The performance was recorded for each pre-train model with hyperparameters of epoch, batch size, and learning rate as 16, 16 and 0.0001 respectively. The prediction results of each model were recorded and compared.from the results of all 12 pre-trained deep learning model, five models that have highest validation accuracy were DenseNet169, DenseNet201, InceptionV3, DenseNet121 and InceptionRestNetV2, respectively.The top-5 highest accuracy models for classifying the COVID-19 were DenseNet169, DenseNet201, InceptionV3, DenseNet121 and InceptionRestnetV2 with accuracies of 95.4%, 95.07%, 94.73%, 94.51% and 93.61% respectively.