Deep Rank-Based Average Pooling Network for Covid-19 Recognition

Wang, Shuihua; Khan, Muhammad Attique; Govindaraj, Vishnuvarthanan; Fernandes, Steven Lawrence; Zhu, Ziquan; Zhang, Yudong

doi:10.32604/cmc.2022.020140

Cited by 57 publications

(36 citation statements)

References 13 publications

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“…Recently, deep learning has been applied to classify COVID-19 scans into infected or normal classes [ 20 , 21 ]. The computer vision (CV) researchers have introduced many techniques using deep learning to classify COVID-19 using CT images [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

Syed

Khan

Tariq

et al. 2021

Behavioural Neurology

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The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional polymerase chain reaction method, has led to an increased number of high-resolution computed tomography (CT) examinations conducted. The manual inspection of the latter, besides being slow, is susceptible to human errors, especially because of an uncanny resemblance between the CT scans of COVID-19 and those of pneumonia, and therefore demands a proportional increase in the number of expert radiologists. Artificial intelligence-based computer-aided diagnosis of COVID-19 using the CT scans has been recently coined, which has proven its effectiveness in terms of accuracy and computation time. In this work, a similar framework for classification of COVID-19 using CT scans is proposed. The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. We demonstrate that once this method is performed on a publicly available dataset, this system attains an improved accuracy of 97.9% and the computational time is almost 34 (sec).

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Section: Introductionmentioning

confidence: 99%

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

Syed

Khan

Tariq

et al. 2021

Behavioural Neurology

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“…The COVID-19 Pneumonia CT dataset also includes a pneumonia class for classification purposes. Moreover, COVID-19 severity detection using deep learning-based segmentation will be considered as a future work as well [63][64][65][66][67][68].…”

Section: Discussionmentioning

confidence: 99%

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

Khan

Alhaisoni

Tariq

et al. 2021

Sensors

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In healthcare, a multitude of data is collected from medical sensors and devices, such as X-ray machines, magnetic resonance imaging, computed tomography (CT), and so on, that can be analyzed by artificial intelligence methods for early diagnosis of diseases. Recently, the outbreak of the COVID-19 disease caused many deaths. Computer vision researchers support medical doctors by employing deep learning techniques on medical images to diagnose COVID-19 patients. Various methods were proposed for COVID-19 case classification. A new automated technique is proposed using parallel fusion and optimization of deep learning models. The proposed technique starts with a contrast enhancement using a combination of top-hat and Wiener filters. Two pre-trained deep learning models (AlexNet and VGG16) are employed and fine-tuned according to target classes (COVID-19 and healthy). Features are extracted and fused using a parallel fusion approach—parallel positive correlation. Optimal features are selected using the entropy-controlled firefly optimization method. The selected features are classified using machine learning classifiers such as multiclass support vector machine (MC-SVM). Experiments were carried out using the Radiopaedia database and achieved an accuracy of 98%. Moreover, a detailed analysis is conducted and shows the improved performance of the proposed scheme.

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“…Moreover, multiple aspects including readability, correctness, completeness, and compactness of documents can be considered to improve the quality of summary. Moreover, the deep learning models will be considered for the data extraction and optimized using metaheuristic techniques [56][57][58][59][60][61][62].…”

Section: Discussionmentioning

confidence: 99%

Improved Text Summarization of News Articles Using GA-HC and PSO-HC

et al. 2021

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Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchal Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.

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Deep Rank-Based Average Pooling Network for Covid-19 Recognition

Cited by 57 publications

References 13 publications

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

Improved Text Summarization of News Articles Using GA-HC and PSO-HC

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