An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

Ezzat, Dalia; Hassanien, Aboul Ella; Ella, Hassan Aboul

doi:10.1016/j.asoc.2020.106742

Cited by 159 publications

(95 citation statements)

References 38 publications

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“…Most of these papers used off-the-shelf networks, including ResNet-18 or ResNet-50 16,17,20,26,29,32,37 , DenseNet-121 27,28,31,32,34 , VGG-16 or VGG-19 19,33,35 , Inception 21,38 and EfficientNet 30,39 , with three considering custom architectures 18,25,36 and three using hand-engineered features [22][23][24] . Most papers classified images into the three classes, that is, COVID-19, non-COVID-19 pneumonia and normal 16,19,21,23,25,26,28,30,[32][33][34][35][36][37] , while two considered an extra class by dividing non-COVID-19 pneumonia into viral and bacterial pneumonia 17,29 . ResNet and DenseNet architectures showed better performance than the others, with accuracies ranging from 0.88 to 0.99.…”

Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

“…Almost all papers had a high (45/62) or unclear (11/62) risk of bias for their participants, with only six assessed as having a low risk of bias. This was primarily due to the following issues: (1) for public datasets it is not possible to know whether patients are truly COVID-19 positive, or if they have underlying selection biases, as anybody can contribute images 16,24,26,28-32,34,35,37,41,44,48,49,76 ; (2) the paper uses only a subset of original datasets, applying some exclusion criteria, without enough details to be reproducible 16,43,44,48,49,51,61,70,71,75,76 ; and/or (3) there are large differences in demographics between the COVID-19 cohort and the control groups, with, for example, paediatric patients as controls 17,24,28,29,31,32,35,37,45,46,59,81 .…”

Section: Risks Of Biasmentioning

confidence: 99%

“…We excluded many studies 18,20,22,23,25,28,29,32,35,43,45,58,71 because it was unclear how many images were used. If a paper only stated the number of patients (and not the number of images), we assumed that there was only one image per patient.…”

Section: Model Evaluation Inmentioning

confidence: 99%

See 2 more Smart Citations

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

et al. 2021

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Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.

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Section: Diagnostic Models For Covid-19 Diagnosis Models Using Cxrsmentioning

confidence: 99%

Section: Risks Of Biasmentioning

confidence: 99%

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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

et al. 2021

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“…The proposed approach is evaluated using different performance metrics such as accuracy, precision, recall, F-score, and confusion matrix. Equation (24) shows the proportion among accurately classified samples to the overall amount of samples. Equation ( 25) explains the error rate (ER).…”

Section: Performance Evaluation Phasementioning

confidence: 99%

“…But the deep learning technique can automatically learn and solve complex problems on its own. Deep learning provides astonishing outcomes when applied to image classification tasks and the complexities associated with deep learning can be overcome by transfer learning [31] using pre-trained architectures [24]. To achieve optimal performance using the pre-trained architectures, the hyperparameters such as learning rate, dropout rate, activation function, number of layers, etc need to be optimized.To tackle these issues, we have proposedMutation based Atom Search Optimization (MASO) algorithm for optimizing the DenseNet 121 architecture for cervical cancer detection.…”

Section: Introductionmentioning

confidence: 99%

Mutation based Atom Search Optimization Algorithm for Hyperparameter Optimization of the DenseNet121 Architecture for Cervical Cancer Classification

Chitra

Kumar

2021

Preprint

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The cervical cancer patient’s death rate can be minimized by accurate and early detection of cervical cancer (CC). One of the popular techniques called the Pap test or Pap smear is widely used for the early detection of CC. The manual analysis consumed more time in the case of CC detection. The existing techniques met few shortcomings in terms of poor accuracy, more computational complexity, higher feature dimensionality, poor reliability, and higher time-consumption with poor hyperparameters optimization. Hence, the computer-aided diagnostic model provides reliable and accurate CC detection at the initial stage. In this paper, we proposed MASO optimized DenseNet 121 architecture for the early detection of cervical cancer. At first, different kinds of augmentation techniques such as horizontal flip, vertical flip, zooming, shearing, height shift, width shift, rotation, and brightness to increase the number of training samples. The Mutation based Atom Search Optimization (MASO) algorithm is established to optimize the hyperparameters in DenseNet 121 architecture suchnumber of neurons in the dense layer, learning rate value, and the batch sizes. Different kinds of performance metrics such as accuracy, specificity, sensitivity, precisions, recall, F-score, and confusion matrix evaluate the performance of MASO optimized DenseNet 121 architecture for CC detection. A single normal class with three abnormal classes namely Carcinoma, Light dysplastic, and Sever dysplastic were selected from the Hervel dataset for experimental investigation. The proposed MASO optimized DenseNet 121 architecture achieves 98.38% accuracy, 98.5% specificity, 98.83% sensitivity, 98.58% precision, 99.3% recall and 98.25% F-score values than other existing methods.

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Thorax computed tomography (CTX) guided ground truth annotation of CHEST radiographs (CXR) for improved classification and detection of COVID‐19

Ertürk,

Toprak,

Cömert

et al. 2024

Numer Methods Biomed Eng

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Several data sets have been collected and various artificial intelligence models have been developed for COVID‐19 classification and detection from both chest radiography (CXR) and thorax computed tomography (CTX) images. However, the pitfalls and shortcomings of these systems significantly limit their clinical use. In this respect, improving the weaknesses of advanced models can be very effective besides developing new ones. The inability to diagnose ground‐glass opacities by conventional CXR has limited the use of this modality in the diagnostic work‐up of COVID‐19. In our study, we investigated whether we could increase the diagnostic efficiency by collecting a novel CXR data set, which contains pneumonic regions that are not visible to the experts and can only be annotated under CTX guidance. We develop an ensemble methodology of well‐established deep CXR models for this new data set and develop a machine learning‐based non‐maximum suppression strategy to boost the performance for challenging CXR images. CTX and CXR images of 379 patients who applied to our hospital with suspected COVID‐19 were evaluated with consensus by seven radiologists. Among these, CXR images of 161 patients who also have had a CTX examination on the same day or until the day before or after and whose CTX findings are compatible with COVID‐19 pneumonia, are selected for annotating. CTX images are arranged in the main section passing through the anterior, middle, and posterior according to the sagittal plane with the reformed maximum intensity projection (MIP) method in the coronal plane. Based on the analysis of coronal MIP reconstructed CTX images, the regions corresponding to the pneumonia foci are annotated manually in CXR images. Radiologically classified posterior to anterior (PA) CXR of 218 patients with negative thorax CTX imaging were classified as COVID‐19 pneumonia negative group. Accordingly, we have collected a new data set using anonymized CXR (JPEG) and CT (DICOM) images, where the PA CXRs contain pneumonic regions that are hidden or not easily recognized and annotated under CTX guidance. The reference finding was the presence of pneumonic infiltration consistent with COVID‐19 on chest CTX examination. COVID‐Net, a specially designed convolutional neural network, was used to detect cases of COVID‐19 among CXRs. Diagnostic performances were evaluated by ROC analysis by applying six COVID‐Net variants (COVIDNet‐CXR3‐A, ‐B, ‐C/COVIDNet‐CXR4‐A, ‐B, ‐C) to the defined data set and combining these models in various ways via ensemble strategies. Finally, a convex optimization strategy is carried out to find the outperforming weighted ensemble of individual models. The mean age of 161 patients with pneumonia was 49.31 ± 15.12, and the median age was 48 years. The mean age of 218 patients without signs of pneumonia in thorax CTX examination was 40.04 ± 14.46, and the median was 38. When working with different combinations of COVID‐Net's six variants, the area under the curve (AUC) using the ensemble COVID‐Net CXR 4A‐4B‐3C was .78, sensitivity 67%, specificity 95%; COVID‐Net CXR 4a‐3b‐3c was .79, sensitivity 69% and specificity 94%. When diverse and complementary COVID‐Net models are used together through an ensemble, it has been determined that the AUC values are close to other studies, and the specificity is significantly higher than other studies in the literature.

show abstract

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

Cited by 159 publications

References 38 publications

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Mutation based Atom Search Optimization Algorithm for Hyperparameter Optimization of the DenseNet121 Architecture for Cervical Cancer Classification

Thorax computed tomography (CTX) guided ground truth annotation of CHEST radiographs (CXR) for improved classification and detection of COVID‐19

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