A review of deep learning-based detection methods for COVID-19

Subramanian, N.; Elharrouss, Omar; Al‐Maadeed, Somaya; Chowdhury, Muhammad E. H.

doi:10.1016/j.compbiomed.2022.105233

Cited by 101 publications

(71 citation statements)

References 74 publications

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“…For those reasons, it seems difficult to compare raw results (in terms of the TPR or the AUC, for example) between the proposed algorithm and the literature, since they do not address exactly the same problem. Moreover, when considering the false negative rate (FNR), defined as the ratio between the images not being assigned to any category (so considered as good health) and the total number of positives, we observed that for most pathologies the FNR was less than 1%, which is a good result (the cited works, by their nature and problem addressed, lead to the calculated FNRs of 1% to 3% [18,19]).…”

Section: Computational Resultsmentioning

confidence: 94%

“…To the best of our knowledge, those methods follow computer science vision but make the abstraction of main practical needs, such as data availability and conditions of use. Moreover, the literature generates good solutions to classify medical images; however, they require high computing power, which implies that these types of solutions cannot be implemented in health centers with few computing resources [18,19]. Indeed, those methods make, in general, the abstraction of computational effort limitations and assume all users can meet the settings used to deploy the proposed algorithms, in terms of both hardware and of software.…”

Section: Introductionmentioning

confidence: 99%

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Balanced Medical Image Classification with Transfer Learning and Convolutional Neural Networks

Benavente

Gatica

González-Feliu

2022

Axioms

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This paper aims to propose a tool for image classification in medical diagnosis decision support, in a context where computational power is limited and then specific, high-speed computing infrastructures cannot be used (mainly for economic and energy consuming reasons). The proposed method combines a deep neural networks algorithm with medical imaging procedures and is implemented to allow an efficient use on affordable hardware. The convolutional neural network (CNN) procedure used VGG16 as its base architecture, using the transfer learning technique with the parameters obtained in the ImageNet competition. Two convolutional blocks and one dense block were added to this architecture. The tool was developed and calibrated on the basis of five common lung diseases using 5430 images from two public datasets and the transfer learning technique. The holdout ratios of 90% and 10% for training and testing, respectively, were obtained, and the regularization tools were dropout, early stopping, and Lasso regularization (L2). An accuracy (ACC) of 56% and an area under the receiver-operating characteristic curve (ROC—AUC) of 50% were reached in testing, which are suitable for decision support in a resource-constrained environment.

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Section: Computational Resultsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Balanced Medical Image Classification with Transfer Learning and Convolutional Neural Networks

Benavente

Gatica

González-Feliu

2022

Axioms

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“…Early work in COVID-19 detection is to extract images on patient lungs using the ultrasound technology, a technique to identify and monitor patients affected by viruses. Therefore, the development of detection and recognition techniques is needed which is capable of automating the process without needing the help of skilled specialists [111,112,113,114]. From these techniques, we can find computer vision based techniques that help in detection using images and videos.…”

Section: Covid-19 Detectionmentioning

confidence: 99%

Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches

Elharrouss¹,

Akbari²,

Almaadeed³

et al. 2022

Preprint

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To understand the real world using various types of data, Artificial Intelligence (AI) is the most used technique nowadays. While finding the pattern within the analyzed data represents the main task. This is performed by extracting representative features step, which is proceeded using the statistical algorithms or using some specific filters. However, the selection of useful features from large-scale data represented a crucial challenge. Now, with the development of convolution neural networks (CNNs), the feature extraction operation has become more automatic and easier. CNNs allow to work on large-scale size of data, as well as cover different scenarios for a specific task. For computer vision tasks, convolutional networks are used to extract features also for the other parts of a deep learning model. The selection of a suitable network for feature extraction or the other parts of a DL model is not random work. So, the implementation of such a model can be related to the target task as well as the computational complexity of it. Many networks have been proposed and become the famous networks used for any DL models in any AI task. These networks are exploited for feature extraction or at the beginning of any DL model which is named backbones. A backbone is a known network trained in many other tasks before and demonstrates its effectiveness. In this paper, an overview of the existing backbones, e.g. VGGs, ResNets, DenseNet, etc, is given with a detailed description. Also, a couple of computer vision tasks are discussed by providing a review of each task regarding the backbones used. In addition, a comparison in terms of performance is also provided, based on the backbone used for each task.

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“…Deep learning and machine learning models are widely used in this context to achieve efficient and autonomous results [2] , [3] . Real-time data can be generated from computer vision systems [4] , [5] , [6] . This can support the human resources that are already present to make informed decisions, avoiding mis-interpretations and overcoming the lack of efficiency in detection.…”

Section: Introductionmentioning

confidence: 99%