COVID-19 detection based on deep learning and artificial bee colony

Sahan, Ali Mohammed; Al-Itbi, Ali Sami; Hameed, Jawad Sami

doi:10.21533/pen.v9i1.1774

Cited by 6 publications

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“…The main advantage of the ABC algorithm over other optimization methods is that global and local searches are performed at each iteration step. In addition, this algorithm is widely used in many studies such as pattern classification, segmentation, and medical image processing (Sahan et al, 2021 ; Zainal Abidin et al, 2020 ) thanks to its ease of implementation and ability to handle objective cost. We used the ABC algorithm in our study to take advantage of these advantages.…”

Section: Methodsmentioning

confidence: 99%

COVID‐19 detection based on pre‐trained deep networks and LSTM model using X‐ray images enhanced contrast with artificial bee colony algorithm

2022

Expert Systems

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Coronavirus (COVID‐19) is an infectious disease that has spread across the world within a short period of time and is causing rapid casualties. The main symptoms of this virus are shortness of breath, fever, cough, and a sore throat. The virus is detected through samples, such as throat swabs and sputum, taken from people who meet the possible case definition and the results are usually obtained within a few hours or a day. The development of test kits to detect the COVID‐19 virus is still an open research topic, and automated and faster diagnostic tools are needed. Recent studies have shown that biomedical images can be used for COVID‐19 testing. This study proposes the hybrid use of pre‐trained deep networks and the long short‐term memory (LSTM) for the classification of COVID‐19 from contrast‐enhanced chest X‐rays. In the proposed system, a transformation function is applied to X‐ray images first. Then, the artificial bee colony (ABC) algorithm is used to optimize the parameters obtained from the transformation function. The pre‐trained deep network models and LSTM are preferred to extract features from the contrast‐enhanced chest X‐rays. At the final stage, COVID‐19, normal (healthy), and pneumonia chest X‐ray are classified using softmax. To evaluate the performance of the proposed method, the “COVID‐19 radiography” dataset, which is widely used in the literature, is preferred. From the proposed model, 98.97% accuracy, 98.80% precision, and 98.70% sensitivity rates are obtained. Experimental results reveal that the proposed model provides efficient results compared to other methods. Thanks to the application of ABC‐based image enhancement, increased classification of 2.5% has been achieved against other state‐of‐the‐art models.

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Section: Methodsmentioning

confidence: 99%

COVID‐19 detection based on pre‐trained deep networks and LSTM model using X‐ray images enhanced contrast with artificial bee colony algorithm

2022

Expert Systems

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A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

et al. 2021

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Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. Meta-Heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, Parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature; however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions. INDEX TERMSCommunity detection; Deep learning; Complex networks; Meta-heuristic algorithms; Parallel computing.

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Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm

Selvaraj¹,

Murugasamy²

2022

AIP

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In recent years, community detection is important because members of the same community share the same concepts. For efficient community detection in a social network, the influence node plays a vital role. A node in the social network or a user that has great influence and power would have a close relationship with a core of the group, termed a community. Therefore, the status of a person is determined by the user's influence strength. That is, a user who has greater influence and strength plays a vital role in the social media community and also acts as a core in the community of the social network. Therefore, a community is a group of nodes in the complex network structure which are interlinked with one another. Effective community detection in a complex structure is a challenging task. Many studies have been done based on topological networks. The approaches are ineffective, inefficient and require more time to process. To overcome these issues, this paper proposes improving the influence nodes in complex networks by using the InfoMap with sigmoid fish swarm optimization algorithm (I-SFSO). Our proposed I-SFSO gives better accuracy rates for the data sets: 92% for Dolphin, 95% for the Facebook dataset, 96% for the Twitter data set, 94% for the YouTube data set, 93% for a karate club and 94% for football.

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COVID-19 detection based on deep learning and artificial bee colony

Cited by 6 publications

References 0 publications

COVID‐19 detection based on pre‐trained deep networks and LSTM model using X‐ray images enhanced contrast with artificial bee colony algorithm

COVID‐19 detection based on pre‐trained deep networks and LSTM model using X‐ray images enhanced contrast with artificial bee colony algorithm

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

Evaluation of Community Detection by Improving Influence Nodes in Complex Networks Using InfoMap with Sigmoid Fish Swarm Optimization Algorithm

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