A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

Shaban, Warda M.; Rabie, Asmaa H.; Saleh, Ahmed I.; Abo-Elsoud, M.A.

doi:10.1016/j.knosys.2020.106270

Cited by 193 publications

(128 citation statements)

References 26 publications

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“…The method by the Ouchicha, Ammor [23] and Narayan Das, Kumar [24] achieved 97.2% and 97.4% overall testing accuracy in an unbalanced dataset where only 219 and 127 COVID-19 images were used for model building. At the same time, Pathak, Shukla [25] and Shaban, Rabie [26] used CT images and achieved 93% accuracy applying small COVID-19 images. Some other studies [16,17,[27][28][29][30][31]introduced different approaches for early detection of COVID-19 using X-ray and CT images indicating lower than or around 90% accuracy rate.…”

Section: Resultsmentioning

confidence: 99%

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

Hasan

Bao

Shawon

2020

Preprint

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Public health and human lives recently have been impacted by the devastating effect of Coronavirus 2019. This catastrophic effect has destroyed the human experience by creating a chaotic healthcare situation infinitely more destructive than the Second World War. Strong communicable characteristics of COVID-19 among human communities make the world’s situation a severe pandemic. Due to the unavailable vaccine of COVID-19 to control rather than cure, early and accurate detection of the virus can be a promising technique for tracking and preventing the infection spreading (e.g., by isolating the patients). This situation indicates to improve auxiliary COVID-19 detection technique. Computed tomography (CT) imaging is a mostly used technique for pneumonia because of its common availability. The application of artificial intelligence systems integrated with images can be a promising alternative for the identification of COVID-19. This paper presents a promising technique of predicting COVID-19 patients from the CT image using convolutional neural networks (CNN). The novel approach is based on DenseNet is the updated CNN architecture in the present state to detect COVID-19. The results outperformed 92% accuracy, with 95% recall showing good performance for the identification of COVID-19.

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Section: Resultsmentioning

confidence: 99%

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

Hasan

Bao

Shawon

2020

Preprint

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“…Based on the experimentation on publicly available hepatitis dataset, the proposed model achieved the highest accuracy of 90.32% as compared to the results from previous study. Finally, GA has recently been applied to improve early COVID-19 patient prediction [31]. They used GA as the wrapper method to find the most relevance features from the chest computed tomography (CT) images for positive and negative COVID-19 subjects.…”

Section: Extreme Gradient Boosting (Xgboost) and Genetic Algorithms (Ga)mentioning

confidence: 99%

“…Genetic algorithms (GAs) have been previously used for feature selection and showed significant results for selecting the best feature sets [24,25]. In the health arena, GA can highly improve the performance of models for emotional stress state detection [26], severe chronic disorders of consciousness prediction [27], children's activity recognition and classification [28], gene encoder [29], hepatitis prediction [30], and COVID-19 patient detection [31].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…Feature selection is used to reduce the dimensionality of data [20,21] and in medical diagnosis, is used to identify most significant features related to disease [22,23]. Genetic algorithm (GA) is a feature selection method that has been used to find the best feature subsets [24,25] and shown significant advantage for improving the performance of emotional stress state detection [26], severe chronic disorders of consciousness prediction [27], children's activities regarding recognition and classification [28], gene encoder [29], hepatitis prediction [30], and coronavirus disease (COVID-19) patient detection [31]. A previous study revealed that a combination of GA and XGBoost has improved the model performance [32].…”

Section: Introductionmentioning

confidence: 99%

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A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

et al. 2020

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Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.

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“…It has been developed based on the architecture of pre-trained Xception model [ 44 ], resulting an overall accuracy of 89.6%. Shaban et al [ 45 ] introduced a new detection strategy of positive COVID-19 patients based on a hybrid selection method of the best image features and an enhanced K-nearest neighbor (EKNN) classifier. The proposed COVID-19 detection strategy has been tested on the chest CT images only.…”

Section: Introductionmentioning

confidence: 99%

Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans

Karar

Hemdan

Shouman

2020

Complex Intell. Syst.

132

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Computer-aided diagnosis (CAD) systems are considered a powerful tool for physicians to support identification of the novel Coronavirus Disease 2019 (COVID-19) using medical imaging modalities. Therefore, this article proposes a new framework of cascaded deep learning classifiers to enhance the performance of these CAD systems for highly suspected COVID-19 and pneumonia diseases in X-ray images. Our proposed deep learning framework constitutes two major advancements as follows. First, complicated multi-label classification of X-ray images have been simplified using a series of binary classifiers for each tested case of the health status. That mimics the clinical situation to diagnose potential diseases for a patient. Second, the cascaded architecture of COVID-19 and pneumonia classifiers is flexible to use different fine-tuned deep learning models simultaneously, achieving the best performance of confirming infected cases. This study includes eleven pre-trained convolutional neural network models, such as Visual Geometry Group Network (VGG) and Residual Neural Network (ResNet). They have been successfully tested and evaluated on public X-ray image dataset for normal and three diseased cases. The results of proposed cascaded classifiers showed that VGG16, ResNet50V2, and Dense Neural Network (DenseNet169) models achieved the best detection accuracy of COVID-19, viral (Non-COVID-19) pneumonia, and bacterial pneumonia images, respectively. Furthermore, the performance of our cascaded deep learning classifiers is superior to other multi-label classification methods of COVID-19 and pneumonia diseases in previous studies. Therefore, the proposed deep learning framework presents a good option to be applied in the clinical routine to assist the diagnostic procedures of COVID-19 infection.

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A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

Cited by 193 publications

References 26 publications

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans

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