Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays

Mukherjee, Himadri; Ghosh, Santosh; Dhar, Ankita; Obaidullah, Sk Md; Santosh, K. C.; Roy, Kaushik

doi:10.1007/s10489-020-01943-6

Cited by 167 publications

(117 citation statements)

References 26 publications

(31 reference statements)

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“…Further modifications were applied to COVID-Net to improve its representational ability for one specific image modality and to make the network computationally more efficient, as in [ 28 ]. Mukherjee et al [ 29 ] proposed a tailored CNN architecture composed of nine layers for detecting COVID-19 positive cases. They trained and tested their network using CT scans and X-rays together.…”

Section: Related Workmentioning

confidence: 99%

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

Alshazly

Linse

Barth

et al. 2021

Sensors

152

102

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This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models’ predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.

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Section: Related Workmentioning

confidence: 99%

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

Alshazly

Linse

Barth

et al. 2021

Sensors

152

102

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“…( 2020 ) CT t-SNE/Grad-CAM Lung Deep learning method Mukherjee et al. ( 2020 ) CT CNN:Tailored DNN Lung Deep learning method Li et al. ( 2020 ) CT Stacked Auto-encoder Lung Deep learning method Kuchana et al.…”

Section: Ai In Diagnostic Of Covid-19mentioning

confidence: 99%

“…Mukherjee et al. ( 2020 ) have developed a lightweight (9-layer) CNN configured DNN that can simultaneously test/train both Chest X-rays and CT scans. The developed system showed a false-negative rate value of 0.02, an AUC value of 0.98, with an overall accuracy value of 96.29 percent.…”

Section: Ai In Diagnostic Of Covid-19mentioning

confidence: 99%

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research

et al. 2021

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Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting in rapid decision-making in saving lives. This article provides an extensive review of AI-based methods to assist medical practitioners with comprehensive knowledge of the efficient AI-based methods for efficient COVID-19 diagnosis. Nearly all the reported methods so far along with their pros and cons as well as recommendations for improvements are discussed, including image acquisition, segmentation, classification, and follow-up diagnosis phases developed between 2019 and 2020. AI and machine learning technologies have boosted the accuracy of Covid-19 diagnosis, and most of the widely used deep learning methods have been implemented and worked well with a small amount of data for COVID-19 diagnosis. This review presents a detailed mythological analysis for the evaluation of AI-based methods used in the process of detecting COVID-19 from medical images. However, due to the quick outbreak of Covid-19, there are not many ground-truth datasets available for the communities. It is necessary to combine clinical experts’ observations and information from images to have a reliable and efficient COVID-19 diagnosis. This paper suggests that future research may focus on multi-modality based models as well as how to select the best model architecture where AI can introduce more intelligence to medical systems to capture the characteristics of diseases by learning from multi-modality data to obtain reliable results for COVID-19 diagnosis for timely treatment .

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“…The aim of the study was to create a model which iteratively learns and adapts to new data without forgetting what it has previously learnt. Another study showed how their network performed equally well for both X-ray and CT images [ 35 ]. The study designed its own deep learning architecture which was trained on 336 Chest X-ray and 336 CT scan images.…”

Section: Similar Literature Study Of Using Deep Learning Algorithms Fmentioning

confidence: 99%

Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks

Sarkar

Vandenhirtz²,

Nagy

et al. 2021

SN COMPUT. SCI.

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The novel Coronavirus, COVID-19, pandemic is being considered the most crucial health calamity of the century. Many organizations have come together during this crisis and created various Deep Learning models for the effective diagnosis of COVID-19 from chest radiography images. For example, The University of Waterloo, along with Darwin AI—a start-up spin-off of this department, has designed the Deep Learning model ‘COVID-Net’ and created a dataset called ‘COVIDx’ consisting of 13,975 images across 13,870 patient cases. In this study, COGNEX’s Deep Learning Software, VisionPro Deep Learning™, is used to classify these Chest X-rays from the COVIDx dataset. The results are compared with the results of COVID-Net and various other state-of-the-art Deep Learning models from the open-source community. Deep Learning tools are often referred to as black boxes because humans cannot interpret how or why a model is classifying an image into a particular class. This problem is addressed by testing VisionPro Deep Learning with two settings, first, by selecting the entire image as the Region of Interest (ROI), and second, by segmenting the lungs in the first step, and then doing the classification step on the segmented lungs only, instead of using the entire image. VisionPro Deep Learning results: on the entire image as the ROI it achieves an overall F score of 94.0%, and on the segmented lungs, it gets an F score of 95.3%, which is better than COVID-Net and other state-of-the-art open-source Deep Learning models.

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Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays

Cited by 167 publications

References 26 publications

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research

Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks

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