Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment

Ibrahim, Mohamed; Youssef, Sherin M.; Fathalla, Karma M.

doi:10.1007/s12652-021-03282-x

Cited by 25 publications

(11 citation statements)

References 50 publications

(108 reference statements)

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“…Next, they presented a method to integrate the abovementioned pretrained method for the general enhancement of the predictive capacity of the model. Ibrahim et al [ 10 ] proposed a new computer-aided framework (COV-CAF) to categorize the severity level of the disease from three-dimensional CT Volumes. COV-CAF integrates conventional and DL methods.…”

Section: Related Workmentioning

confidence: 99%

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification

Dutta¹,

Aljarallah

Abirami³

et al. 2022

Journal of Healthcare Engineering

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Intelligent decision support systems (IDSS) for complex healthcare applications aim to examine a large quantity of complex healthcare data to assist doctors, researchers, pathologists, and other healthcare professionals. A decision support system (DSS) is an intelligent system that provides improved assistance in various stages of health-related disease diagnosis. At the same time, the SARS-CoV-2 infection that causes COVID-19 disease has spread globally from the beginning of 2020. Several research works reported that the imaging pattern based on computed tomography (CT) can be utilized to detect SARS-CoV-2. Earlier identification and detection of the diseases is essential to offer adequate treatment and avoid the severity of the disease. With this motivation, this study develops an efficient deep-learning-based fusion model with swarm intelligence (EDLFM-SI) for SARS-CoV-2 identification. The proposed EDLFM-SI technique aims to detect and classify the SARS-CoV-2 infection or not. Also, the EDLFM-SI technique comprises various processes, namely, data augmentation, preprocessing, feature extraction, and classification. Moreover, a fusion of capsule network (CapsNet) and MobileNet based feature extractors are employed. Besides, a water strider algorithm (WSA) is applied to fine-tune the hyperparameters involved in the DL models. Finally, a cascaded neural network (CNN) classifier is applied for detecting the existence of SARS-CoV-2. In order to showcase the improved performance of the EDLFM-SI technique, a wide range of simulations take place on the COVID-19 CT data set and the SARS-CoV-2 CT scan data set. The simulation outcomes highlighted the supremacy of the EDLFM-SI technique over the recent approaches.

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Section: Related Workmentioning

confidence: 99%

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification

Dutta¹,

Aljarallah

Abirami³

et al. 2022

Journal of Healthcare Engineering

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“…For instance, ( Foysal & Aowlad Hossain, 2021 ) developed an ensemble of shallow CNNs to distinguish between COVID-19 positive and negative images, attaining accuracies and sensitivities of .96 and 0.97 respectively. ( Ibrahim et al, 2021 ) developed a modified version of VGG16 – Norm-VGG16 – which attains an accuracy and sensitivity of 0.978 and 0.967, respectively. ( Oyelade et al, 2021 ) propose a new deep learning framework – CovFrameNet – that attains a recall of 0.85, F1 score of 0.9, and specificity of 1.0 in detecting COVID-19.…”

Section: Related Workmentioning

confidence: 99%

Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT

Garg

Salehi

Rocca

et al. 2022

Expert Systems with Applications

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With COVID-19 cases rising rapidly, deep learning has emerged as a promising diagnosis technique. However, identifying the most accurate models to characterize COVID-19 patients is challenging because comparing results obtained with different types of data and acquisition processes is non-trivial. In this paper we designed, evaluated, and compared the performance of 20 convolutional neutral networks in classifying patients as COVID-19 positive, healthy, or suffering from other pulmonary lung infections based on Chest CT scans, serving as the first to consider the EfficientNet family for COVID-19 diagnosis and employ intermediate activation maps for visualizing model performance. All models are trained and evaluated in Python using 4173 Chest CT images from the dataset entitled “A COVID multiclass dataset of CT scans,” with 2168, 758, and 1247 images of patients that are COVID-19 positive, healthy, or suffering from other pulmonary infections, respectively. EfficientNet-B5 was identified as the best model with an F1 score of 0.9769±0.0046, accuracy of 0.9759±0.0048, sensitivity of 0.9788±0.0055, specificity of 0.9730±0.0057, and precision of 0.9751± 0.0051. On an alternate 2-class dataset, EfficientNetB5 obtained an accuracy of 0.9845±0.0109, F1 score of 0.9599±0.0251, sensitivity of 0.9682±0.0099, specificity of 0.9883±0.0150, and precision of 0.9526 ± 0.0523. Intermediate activation maps and Gradient-weighted Class Activation Mappings offered human-interpretable evidence of the model’s perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools. With a prediction speed of under 0.1 seconds on GPUs and 0.5 seconds on CPUs, our proposed model offers a rapid, scalable, and accurate diagnostic for COVID-19.

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“…The analysis of clusters 15,16 is general statistical technique having victorious applications to COVID‐19, and is most widely considered as epidemiology. With respect to group data points, the analysis of cluster is utilized for studying infectious diseases, non‐communicable diseases and pandemic outbreaks, like SARS, Ebola, and COVID‐19 17 . Clustering techniques are altered and common instances are spectral clustering and K‐means clustering that divides the attribute into discrete sets and hierarchical clustering that does not devise an accurate count of clusters 18–20 .…”

Section: Introductionmentioning

confidence: 99%

“…With respect to group data points, the analysis of cluster is utilized for studying infectious diseases, non-communicable diseases and pandemic outbreaks, like SARS, Ebola, and COVID-19. 17 Clustering techniques are altered and common instances are spectral clustering and K-means clustering that divides the attribute into discrete sets and hierarchical clustering that does not devise an accurate count of clusters. [18][19][20] Various techniques 21 are devised for determining the initial centroids of cluster.…”

mentioning

confidence: 99%

Poor and rich dolphin optimization algorithm with modified deep fuzzy clustering for COVID‐19 patient analysis

Dhandapani¹,

J²

2022

Concurrency and Computation

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The Coronavirus disease 2019 is considered as a pandemic by the World Health Organization (WHO), which has spread worldwide. Over millions of peoples are infected across the globe and several people are died. However, the most worrying group of patients suffered from lung severity with respiratory failure. Hence, cluster analysis is utilized for examining the heterogeneity of diseases for determining various clinical phenotypes having the same traits. This article devises an optimization-driven technique for COVID-19 patient analysis using the spark framework. Here, the input data is partitioned and fed to different slave nodes. In slave node, the selection of imperative features is done using the proposed poor and rich dolphin optimization algorithm (PRDOA). The proposed PRDOA is obtained by combining poor and rich (PRO) and dolphin echolation (DE) algorithm. The fitness is newly devised considering Minkowski distance measure. The clustering is performed on the master node using the proposed Tanimoto-based deep fuzzy clustering (TDFC) for effective COVID-19 patient analysis. Thus, the proposed TDFC is obtained by incorporating Tanimoto concept and deep fuzzy clustering. The proposed PRDOA with TDFC offered enhanced performance with the highest clustering accuracy of 89.8%, dice coefficient of 90%, Jaccard coefficient of 85.7%, and rand coefficient of 85.7%.

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Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment

Cited by 25 publications

References 50 publications

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification

Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT

Poor and rich dolphin optimization algorithm with modified deep fuzzy clustering for COVID‐19 patient analysis

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