Transfer learning for establishment of recognition of COVID-19 on CT imaging using small-sized training datasets

Abstract

The coronavirus disease, called COVID-19, which is spreading fast worldwide since the end of 2019, and has become a global challenging pandemic. Until 27th May 2020, it caused more than 5.6 million individuals infected throughout the world and resulted in greater than 348,145 deaths. CT images-based classification technique has been tried to use the identification of COVID-19 with CT imaging by hospitals, which aims to minimize the possibility of virus transmission and alleviate the burden of clinicians and radiologists. Early diagnosis of COVID-19, which not only prevents the disease from spreading further but allows more reasonable allocation of limited medical resources. Therefore, CT images play an essential role in identifying cases of COVID-19 that are in great need of intensive clinical care. Unfortunately, the current public health emergency, which has caused great difficulties in collecting a large set of precise data for training neural networks. To tackle this challenge, our first thought is transfer learning, which is a technique that aims to transfer the knowledge from one or more source tasks to a target task when the latter has fewer training data. Since the training data is relatively limited, so a transfer learning-based DensNet-121 approach for the identification of COVID-19 is established. The proposed method is inspired by the precious work of predecessors such as CheXNet for identifying common Pneumonia, which was trained using the large Chest X-ray14 dataset, and the dataset contains 112,120 frontal chest X-rays of 14 different chest diseases (including Pneumonia) that are individually labeled and achieved good performance. Therefore, CheXNet as the pre-trained network was used for the target task (COVID-19 classification) by fine-tuning the network weights on the small-sized dataset in the target task. Finally, we evaluated our proposed method on the COVID-19-CT dataset. Experimentally, our method achieves state-of-the-art performance for the accuracy (ACC) and F1-score. The quantitative indicators show that the proposed method only uses a GPU can reach the best performance, up to 0.87 and 0.86, respectively, compared with some widely used and recent deep learning methods, which are helpful for COVID-19 diagnosis and patient triage. The codes used in this manuscript are publicly available on GitHub at ( https://github.com/lichun0503/CT-Classification ).

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

“…However, the amount of labeled radiology images used for training limits the performance and generalization of those models [ 59 ]. In order to address this issue, several transfer learning-based approaches for COVID-19 identification have been developed in the literature [ 29 , 41 ].…”

Hybrid deep learning of social media big data for predicting the evolution of COVID-19 transmission

“…On the clinical scale, deep convolutional neural networks (DCNN) are developed to automatically identify COVID-19’s infections and regions of interest (ROI) via medical images in an economical and efficient manner [23] , which can be especially helpful for the less-developed communities. Pre-trained DL models such as ResNet, U-Net, VB-Net, and others, have also been employed for X-ray or CT image segmentation in COVID-19 applications [23] , which have generated promising results in distinguishing COVID-19 from community-acquired pneumonia by segmenting and locating infected lung regions and lesions, and hence tracking and evaluating the virus’ severity and evolution over time [24] .…”

“…On the clinical scale, deep convolutional neural networks (DCNN) are developed to automatically identify COVID-19’s infections and regions of interest (ROI) via medical images in an economical and efficient manner [23] , which can be especially helpful for the less-developed communities. Pre-trained DL models such as ResNet, U-Net, VB-Net, and others, have also been employed for X-ray or CT image segmentation in COVID-19 applications [23] , which have generated promising results in distinguishing COVID-19 from community-acquired pneumonia by segmenting and locating infected lung regions and lesions, and hence tracking and evaluating the virus’ severity and evolution over time [24] . On the other hand, as numerical data pertaining to the number of suspected, confirmed, cured, and death COVID-19 cases, passengers travel trajectories, etc., are being shared widely on the internet daily, traditional and novel ML methods can be applied to learn from the vastly available information to forecast the transmission of COVID-19 [25] .…”

A Literature Review: Detection of COVID-19 in Computed Tomography Images Using Deep Learning

“…O trabalho realizado por [Turkoglu 2021], apresenta um método em Multiple Kernels-ELM (MKs-ELM-DNN), para a detecc ¸ão de COVID-19 por meio de imagens de TC de tórax utilizando a rede DenseNet201. Já na pesquisa dos autores [Li et al 2021], foi proposto um método (CheXNet) baseado em transferência de aprendizagem que visa identificar COVID-19 em imagens de TC de tórax. No trabalho de [Arora et al 2021], é feito o uso da Super Resoluc ¸ão (SR) no pré-processamento das imagens com técnicas de aumento de dados e a transferência de aprendizagem.…”