Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Hasan, Ali M.; Al-Jawad, Mohammed M. Hassoun; Jalab, Hamid A.; Shaiba, Hadil; Ibrahim, Rabha W.; Al-Shamasneh, Ala’a R.

doi:10.3390/e22050517

Cited by 142 publications

(112 citation statements)

References 38 publications

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“…The length of the feature space for the handcrafted features was 772. As mentioned in related image classification problems that combining DL and handcrafted features may enhance the performance of classification problems (Hasan et al, 2020;Wei et al, 2019a;Hansley, Segundo & Sarkar, 2018). Thus, in the third stage, the FUSI-CAD system was constructed by fusing both the DL and HC features.…”

Section: Feature Fusion Stepmentioning

confidence: 99%

“…The DenseNet is a novel CNN architecture, which can perform well in case of trained with a huge number of images; however, it has a high complexity and a large number of layers, which increase the chances of overfitting in case of trained with inadequate number of images. Hasan et al (2020) proposed a hybrid approach based on CNN, Q-deformed entropy, and long-short-term-memory (LSTM) network and accomplished an accuracy of 99.68%. The advantage of this method is that the authors constructed a new CNN with few number of convolutional layers to decrease the over-fitting by reducing the CNN construction complexity.…”

Section: Introductionmentioning

confidence: 99%

“…They can also face the problem of overfitting when the training dataset is not large (Zhang et al, 2017;Nanni, Ghidoni & Brahnam, 2017). Fusing DL and handcrafted features may improve the performance of the image classification problems (Hasan et al, 2020;Wei et al, 2019a;Hansley, Segundo & Sarkar, 2018). Therefore, in this study, we proposed an efficient novel CAD system called FUSI-CAD, which investigates the effect of fusing handcrafted features and DL features extracted from multiple CNNs trained with COVID-19 CT images.…”

Section: Introductionmentioning

confidence: 99%

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FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features

Ragab¹,

Attallah²

2020

PeerJ Computer Science

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The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In addition, it prevents the spread of this pandemic virus. Computer-aided diagnosis (CAD) based on artificial intelligence (AI) techniques can be used to distinguish between COVID-19 and non-COVID-19 from the computed tomography (CT) imaging. Furthermore, the CAD systems are capable of delivering an accurate faster COVID-19 diagnosis, which consequently saves time for the disease control and provides an efficient diagnosis compared to laboratory tests. In this study, a novel CAD system called FUSI-CAD based on AI techniques is proposed. Almost all the methods in the literature are based on individual convolutional neural networks (CNN). Consequently, the FUSI-CAD system is based on the fusion of multiple different CNN architectures with three handcrafted features including statistical features and textural analysis features such as discrete wavelet transform (DWT), and the grey level co-occurrence matrix (GLCM) which were not previously utilized in coronavirus diagnosis. The SARS-CoV-2 CT-scan dataset is used to test the performance of the proposed FUSI-CAD. The results show that the proposed system could accurately differentiate between COVID-19 and non-COVID-19 images, as the accuracy achieved is 99%. Additionally, the system proved to be reliable as well. This is because the sensitivity, specificity, and precision attained to 99%. In addition, the diagnostics odds ratio (DOR) is ≥ 100. Furthermore, the results are compared with recent related studies based on the same dataset. The comparison verifies the competence of the proposed FUSI-CAD over the other related CAD systems. Thus, the novel FUSI-CAD system can be employed in real diagnostic scenarios for achieving accurate testing for COVID-19 and avoiding human misdiagnosis that might exist due to human fatigue. It can also reduce the time and exertion made by the radiologists during the examination process.

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Section: Feature Fusion Stepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features

Ragab¹,

Attallah²

2020

PeerJ Computer Science

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“…The results of the generalized fractional entropy are examined both in usual probability distributions and data series. Moreover, by using the quantum deformed calculus, Hasan et al [ 25 ] introduced a generalized q-entropy.…”

Section: The Sise Dynamical System Involves Tsallis Entropymentioning

confidence: 99%

Susceptible-Infected-Susceptible Epidemic Discrete Dynamic System Based on Tsallis Entropy

Momani

Ibrahim

Hadid

2020

Entropy

Self Cite

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This investigation deals with a discrete dynamic system of susceptible-infected-susceptible epidemic (SISE) using the Tsallis entropy. We investigate the positive and maximal solutions of the system. Stability and equilibrium are studied. Moreover, based on the Tsallis entropy, we shall formulate a new design for the basic reproductive ratio. Finally, we apply the results on live data regarding COVID-19.

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“…Along with that, Waheed et al [16] developed an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN to generate synthetic chest X-ray (CXR) images. Hasan et al [17] study extract features from CT images using deep learning and a Q-deformed entropy algorithm to classify COVID-19, pneumonia, and normal cases after that features are classified using a long short-term memory (LSTM) neural network classifier. They achieved 99.68% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification Approach for Detecting COVID-19 using Deep Convolutional Neural Networks

Sarker¹,

Tan²,

Ma³

et al. 2020

Preprint

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The COVID-19 pandemic situation has created even more difficulties in the quick identification and screening of the COVID-19 patients for the medical specialists. Therefore, a significant study is necessary for detecting COVID-19 cases using an automated diagnosis method, which can aid in controlling the spreading of the virus. In this paper, the study suggests a Deep Convolutional Neural Network-based multi-classification approach (COV-MCNet) using eight different pre-trained architectures such as VGG16, VGG19, ResNet50V2, DenseNet201, InceptionV3, MobileNet, InceptionResNetV2, Xception which are trained and tested on the X-ray images of COVID-19, Normal, Viral Pneumonia, and Bacterial Pneumonia. The results from 3-class (Normal vs. COVID-19 vs. Viral Pneumonia) showed that only the ResNet50V2 model provides the highest classification performance (accuracy: 95.83%, precision: 96.12%, recall: 96.11%, F1-score: 96.11%, specificity: 97.84%) compared to rest of the models. The results from 4-class (Normal vs. COVID-19 vs. Viral Pneumonia vs. Bacterial Pneumonia) demonstrated that the pre-trained model DenseNet201 provides the highest classification performance (accuracy: 92.54%, precision: 93.05%, recall: 92.81%, F1-score: 92.83%, specificity: 97.47%). Notably, the ResNet50V2 (3-class) and DenseNet201 (4-class) models in the proposed COV-MCNet framework showed higher accuracy compared to the rest six models. This indicates that the designed system can produce promising results to detect the COVID-19 cases on the availability of more data. The proposed multi-classification network (COV-MCNet) significantly speeds up the existing radiology-based method, which will be helpful to the medical community and clinical specialists for early diagnosis of the COVID-19 cases during this pandemic.

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Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Cited by 142 publications

References 38 publications

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features

Susceptible-Infected-Susceptible Epidemic Discrete Dynamic System Based on Tsallis Entropy

Automatic Classification Approach for Detecting COVID-19 using Deep Convolutional Neural Networks

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