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Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier

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Abraham B. , Nair M. S.

tom Vol. 40, no. 4

1436--1445

Corona virus disease-2019 (COVID-19) is a pandemic caused by novel coronavirus. COVID-19 is spreading rapidly throughout the world. The gold standard for diagnosing COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR) test. However, the facility for RT-PCR test is limited, which causes early diagnosis of the disease difficult. Easily available modalities like X-ray can be used to detect specific symptoms associated with COVID-19. Pre-trained convolutional neural networks are widely used for computer-aided detection of diseases from smaller datasets. This paper investigates the effectiveness of multi-CNN, a combination of several pre-trained CNNs, for the automated detection of COVID-19 from X-ray images. The method uses a combination of features extracted from multi-CNN with correlation based feature selection (CFS) technique and Bayesnet classifier for the prediction of COVID-19. The method was tested using two public datasets and achieved promising results on both the datasets. In the first dataset consisting of 453 COVID-19 images and 497 non-COVID images, the method achieved an AUC of 0.963 and an accuracy of 91.16%. In the second dataset consisting of 71 COVID-19 images and 7 non-COVID images, the method achieved an AUC of 0.911 and an accuracy of 97.44%. The experiments performed in this study proved the effectiveness of pre-trained multi-CNN over single CNN in the detection of COVID-19.

AutoCovNet: Unsupervised feature learning using autoencoder and feature merging for detection of COVID-19 from chest X-ray images

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Rashid N. , Hossain M. A. F. , Ali M. , Islam Sukanya M. , Mahmud T. , Fattah S. A.

2021

tom Vol. 41, no. 4

1685--1701

With the onset of the COVID-19 pandemic, the automated diagnosis has become one of the most trending topics of research for faster mass screening. Deep learning-based approaches have been established as the most promising methods in this regard. However, the limitation of the labeled data is the main bottleneck of the data-hungry deep learning methods. In this paper, a two-stage deep CNN based scheme is proposed to detect COVID-19 from chest X-ray images for achieving optimum performance with limited training images. In the first stage, an encoder-decoder based autoencoder network is proposed, trained on chest X-ray images in an unsupervised manner, and the network learns to reconstruct the X-ray images. An encoder-merging network is proposed for the second stage that consists of different layers of the encoder model followed by a merging network. Here the encoder model is initialized with the weights learned on the first stage and the outputs from different layers of the encoder model are used effectively by being connected to a proposed merging network. An intelligent feature merging scheme is introduced in the proposed merging network. Finally, the encoder-merging network is trained for feature extraction of the X-ray images in a supervised manner and resulting features are used in the classification layers of the proposed architecture. Considering the final classification task, an EfficientNet-B4 network is utilized in both stages. An end to end training is performed for datasets containing classes: COVID-19, Normal, Bacterial Pneumonia, Viral Pneumonia. The proposed method offers very satisfactory performances compared to the state of the art methods and achieves an accuracy of 90:13% on the 4-class, 96:45% on a 3-class, and 99:39% on 2-class classification.

Obrazowanie rentgenowskie – nie tylko pomiar osłabienia promieniowania

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Skrzyński W.

tom Vol. 9, nr 1

15--18

Medyczne obrazowanie rentgenowskie jest oparte na pomiarze osłabienia wiązki promieniowania przechodzącej przez tkanki pacjenta. Osłabienie wiązki nie jest jednak jedynym efektem oddziaływania promieniowania w tkankach. Oprócz osłabienia obserwujemy rozproszenie (także wsteczne), tworzenie promieniowania charakterystycznego (wymuszona fluorescencja), a nawet zmianę fazy i załamanie fali elektromagnetycznej. Artykuł zawiera krótki przegląd literatury dotyczącej możliwych zastosowań tych zjawisk w obrazowaniu medycznym.

X-ray medical imaging is based on measuring the attenuation of the radiation beam passing through the patient’s tissues. However, beam attenuation is not the only effect of radiation interaction in tissues. In addition to attenuation, we can also observe scattering (including backscattering), creation of characteristic radiation (X-ray fluoerescence), and even phasechange and refraction of the electromagnetic wave. The article contains a brief review of the literature on the possible applications of these phenomena in medical imaging.