Covid-19 Diagnostic Using 3d Deep Transfer Learning for Classification of Volumetric Computerised Tomography Chest Scans

Xue, Shuohan; Abhayaratne, Charith

doi:10.1109/icassp39728.2021.9414947

Cited by 25 publications

(20 citation statements)

References 9 publications

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“…In the 2021 COVID-19 SPGC challenge [7], six conference papers were accepted, which corresponded to the approaches that achieved the best results [23][24][25][26][27]. TheSaviours [25] team proposed a two-stage convolutional neural network approach.…”

Section: Related Workmentioning

confidence: 99%

Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Bougourzi

Contino

Distante

et al. 2021

Sensors

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Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets.

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Section: Related Workmentioning

confidence: 99%

Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Bougourzi

Contino

Distante

et al. 2021

Sensors

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“…This is called 2D+1D network [15], [9], [4]. The third method is a pure 3D CNN network, where slice annotation is not needed, and a set of or all the available slice images are used as input, and the 3D network process all these input images all at once in a 3D channel space [23], [24], [7].…”

Section: Related Workmentioning

confidence: 99%

“…They cut off a portion of slice images at the beginning and end of a CT-scan volume. In [24], the authors first segment the lung mask from a slice image using traditional morphological transforms, then use this mask to select good slice images and generate lung-only images (no background, bone or tissue) slice images. To make the number of images a fixed number, they use 3D cubic interpolation to regenerate slice images.…”

Section: Related Workmentioning

confidence: 99%

“…The second goal is to segment the lung mask of a slice image, so a masked image, where background, bones, vessels, tissues are all blacked out. This has been shown useful in [9], [8], [24].…”

Section: Data Preprocessing and Preparationmentioning

confidence: 99%

“…Previous works show that some slice images, particularly those of closed lungs, are useless in classification the CTscan volume [17], [23], [24]. Therefore, it is beneficial to filter out these closed-lung slice images.…”

Section: Selection Of Slice Imagesmentioning

confidence: 99%

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A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images

Tan¹,

Liu²

2021

Preprint

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We present an automatic COVID1-19 diagnosis framework from lung CT-scan slice images. In this framework, the slice images of a CT-scan volume are first proprocessed using segmentation techniques to filter out images of closed lung, and to remove the useless background. Then a resampling method is used to select a set of fixed number of slice images for training and validation. A 3D CNN network with BERT is used to classify this set of selected slice images. In this network, an embedding feature is also extracted. In cases where there are more than one set of slice images in a volume, the features of all sets are extracted and pooled into a feature vector for the whole CT-scan volume. A simple multiple-layer perceptron (MLP) network is to used to further classify the aggregated feature vector. The models are trained and evaluated on the provided training and validation datasets. On the validation dataset, the precision is 0.906 and the F1 score is 0.89.

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Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review

2022

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Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.

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Covid-19 Diagnostic Using 3d Deep Transfer Learning for Classification of Volumetric Computerised Tomography Chest Scans

Cited by 25 publications

References 9 publications

Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images

Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review

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