COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

Abstract

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitable Convolutional Neural Network (CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.

Developing an efficient deep neural network for automatic detection of COVID-19 using chest X-ray images

“…The final accuracy reported by these researchers for separating the two classes is reported to be about 96%. Horri et al [30] used 3 types of medical imaging (X-ray, Ultrasound, and CT) to automatically detect the two classes of COVID-19 and healthy. In their research, they used an optimized deep VGG transfer learning network.…”

Machine Learning for Mortality Analysis in Patients with COVID-19

“…On the other hand, recent studies have shown that machine/deep learning techniques are technologies used in all branches of health sciences as elements of clinical decision support and as generators of new clinical knowledge [ 19 , 20 , 21 ]. In this regard, multiple research projects have been published using machine/deep learning techniques for the early detection of the COVID-19 virus [ 22 , 23 , 24 , 25 ], models applied to patients admitted to ICUs, which have been the clinical units most affected by the virus [ 26 , 27 , 28 ], and machine/deep learning applied in “omic” technologies to predict complications of COVID-19 [ 29 , 30 ]. The number of published papers about COVID-19 is continuously growing [ 31 , 32 , 33 ].…”

Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data

“…The first four blocks of the VGG16 architecture pre-trained on ImageNet weights are used for this purpose [13,17]. Since the Image net set is non-overlapping to the problem, the last 8 layers, i.e., the third and fourth convolution blocks are fine-tuned on the augmented CT scan training data [40]. While training these, it is desired that the fourth block adapts more to the data compared to the third block.…”