COVID-19 detection in chest X-ray images using deep boosted hybrid learning

Khan, Saddam Hussain; Sohail, Anabia; Khan, Asifullah; Hassan, Mehdi; Lee, Yeon Soo; Alam, Jamshed; Basit, Abdul; Zubair, Saima

doi:10.1016/j.compbiomed.2021.104816

Cited by 72 publications

(46 citation statements)

References 38 publications

(28 reference statements)

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“…Finally, a detailed comparative study was conducted between the proposed QSGOA-DL technique and other recent approaches, and the results are shown in Table 2 and Figures 10 and 11 [ 26 ]. By examining the results in terms of precision, it is evident that DHL-2, ResNet-1, and ResNet-2 techniques attained a minimal precision of 97%, 97%, and 97%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification

Ragab

Alshehri

Alhakamy

et al. 2022

Journal of Healthcare Engineering

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Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology scan is a significant tool for early management and diagnosis of COVID-19 since the virus targets the respiratory system. Chest X-ray (CXR) images are highly useful in the effective detection of COVID-19, thanks to its availability, cost-effective means, and rapid outcomes. In addition, Artificial Intelligence (AI) techniques such as deep learning (DL) models play a significant role in designing automated diagnostic processes using CXR images. With this motivation, the current study presents a new Quantum Seagull Optimization Algorithm with DL-based COVID-19 diagnosis model, named QSGOA-DL technique. The proposed QSGOA-DL technique intends to detect and classify COVID-19 with the help of CXR images. In this regard, the QSGOA-DL technique involves the design of EfficientNet-B4 as a feature extractor, whereas hyperparameter optimization is carried out with the help of QSGOA technique. Moreover, the classification process is performed by a multilayer extreme learning machine (MELM) model. The novelty of the study lies in the designing of QSGOA for hyperparameter optimization of the EfficientNet-B4 model. An extensive series of simulations was carried out on the benchmark test CXR dataset, and the results were assessed under different aspects. The simulation results demonstrate the promising performance of the proposed QSGOA-DL technique compared to recent approaches.

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Section: Resultsmentioning

confidence: 99%

Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification

Ragab

Alshehri

Alhakamy

et al. 2022

Journal of Healthcare Engineering

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“…We employ three different ML classifiers for classification: SVM [ 34 ], MLP [ 35 ], and AdaBoostM1 [ 36 ]. In DFS-HL, deep CNNs minimize the empirical risk and reduce training error during optimal hyper-parameter selection [ 37 ]. In addition, ML classifiers aim to minimize the test error on the unseen data with a fixed distribution for the training set by exploiting the structural risk minimization principle and improving generalization.…”

Section: Anticipated Methodologymentioning

confidence: 99%

“…Dynamic features are extracted by using the proposed BRAIN-RENet from the second last layer. Features fusion with hybrid learning exploited the advantages of empirical and structural risk minimization to enhance the performance of the brain tumor classification stage [ 37 ]. Deep CNNs contain strong learning ability and focus on reducing the empirical risk factor to minimize the training loss and to avoid overfitting [ 33 ].…”

Section: Anticipated Methodologymentioning

confidence: 99%

A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI

Zahoor

Qureshi

Bibi

et al. 2022

Sensors

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Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.

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“…The malware classification challenge is addressed by a hybrid learning scheme and named as Deep Feature Space-based Malware Classification (DFS-MC). The classification ability of the proposed framework is enhanced by combining the benefits of both empirical and structural risk minimization [29]. CNNs are high-capacity learning models and follow the principles of empirical risk minimization learning theory, which focuses on minimizing training loss.…”

Section: The Proposed Deep Feature Space-based Malware Classification (Dfs-mc)mentioning

confidence: 99%

Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning

et al. 2021

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Malware is a key component of cyber-crime, and its analysis is the first line of defence against cyber-attack. This study proposes two new malware classification frameworks: Deep Feature Space-based Malware classification (DFS-MC) and Deep Boosted Feature Space-based Malware classification (DBFS-MC). In the proposed DFS-MC framework, deep features are generated from the customized CNN architectures and are fed to a support vector machine (SVM) algorithm for malware classification, while, in the DBFS-MC framework, the discrimination power is enhanced by first combining deep feature spaces of two customized CNN architectures to achieve boosted feature spaces. Further, the detection of exceptional malware is performed by providing the deep boosted feature space to SVM. The performance of the proposed malware classification frameworks is evaluated on the MalImg malware dataset using the hold-out cross-validation technique. Malware variants like Autorun.K, Swizzor.gen!I, Wintrim.BX and Yuner.A is hard to be correctly classified due to their minor inter-class differences in their features. The proposed DBFS-MC improved performance for these difficult to discriminate malware classes using the idea of feature boosting generated through customized CNNs. The proposed classification framework DBFS-MC showed good results in term of accuracy: 98.61%, F-score: 0.96, precision: 0.96, and recall: 0.96 on stringent test data, using 40% unseen data.

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COVID-19 detection in chest X-ray images using deep boosted hybrid learning

Cited by 72 publications

References 38 publications

Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification

Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification

A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI

Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning

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