COVIDXception-Net: A Bayesian Optimization-Based Deep Learning Approach to Diagnose COVID-19 from X-Ray Images

Arman, Shifat E; Rahman, Sejuti; Deowan, Shamim Ahmed

doi:10.1007/s42979-021-00980-3

Cited by 8 publications

(4 citation statements)

References 52 publications

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“…It classified the data in the normal class with 98.04% accuracy, the data in the Pneumonia class with 96.73% accuracy, and the data in the COVID-19 class with 100% accuracy. Arman et al [ 18 ] optimized the HP values of VGG16, MobileNetV2, InceptionV3, and Xception architectures using BO to detect COVID-19 on chest X-ray images. The proposed method classified three classes of X-ray images labeled Normal, Pneumonia and COVID.…”

Section: Related Workmentioning

confidence: 99%

COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms

Canayaz

Şehrïbanoğlu

Ozdag

et al. 2022

Neural Comput & Applic

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Early diagnosis of COVID-19, the new coronavirus disease, is considered important for the treatment and control of this disease. The diagnosis of COVID-19 is based on two basic approaches of laboratory and chest radiography, and there has been a significant increase in studies performed in recent months by using chest computed tomography (CT) scans and artificial intelligence techniques. Classification of patient CT scans results in a serious loss of radiology professionals' valuable time. Considering the rapid increase in COVID-19 infections, in order to automate the analysis of CT scans and minimize this loss of time, in this paper a new method is proposed using BO (BO)-based MobilNetv2, ResNet-50 models, SVM and kNN machine learning algorithms. In this method, an accuracy of 99.37% was achieved with an average precision of 99.38%, 99.36% recall and 99.37% F-score on datasets containing COVID and non-COVID classes. When we examine the performance results of the proposed method, it is predicted that it can be used as a decision support mechanism with high classification success for the diagnosis of COVID-19 with CT scans.

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Section: Related Workmentioning

confidence: 99%

COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms

Canayaz

Şehrïbanoğlu

Ozdag

et al. 2022

Neural Comput & Applic

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“…The rapid and widespread spread of COVID-19 has resulted in severe human and financial losses, and has had a significant impact on the healthcare system. There is an urgent need for early prediction of COVID-19 for effective future forecasting and efficient pandemic-tackling policy regulations, reducing strain on the medical care system [1][2][3][4][5][6]. Despite efforts to control the spread of the virus, an increase in the number of patients with viral mutations continues to be a major concern worldwide.…”

Section: Introductionmentioning

confidence: 99%

“…This study will allow the precise prediction of COVID-19 cases and help the world fight the pandemic by developing efficient strategies for productive future forecasting. In addition, Bayesian optimization was used to test the different models before and after optimization [2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Tariq,

Ismail

2024

PLoS ONE

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The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron’s, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

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“…The automotive industry in Indonesia experienced a 65% decline in sales during the COVID-19 pandemic (Arman, Rahman and Deowan, 2022). Currently, many automotive companies are starting to bounce back from their downturn, as evidenced by a large number of market demands, including the automotive industry for two-wheeled vehicles (Filho and Simão, 2022).…”

Section: Introductionmentioning

confidence: 99%

Optimisation of the Partial Runner Set-up Process in Plastic Injection Moulds Using Eight Steps in the Motorcycle Industry

Setiawan,

Perdana,

Aisa

2024

QIP Journal

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Purpose: This study aims to analyse the causes of partial set-up work that is not optimal and provide solutions to the sub-optimal set-up work on family-type moulds in type A. Methodology/Approach: This study uses an 8-Step improvement approach combined with several methods, including Focus Group Discussion (FGD), Quality Cost Delivery Safety Moral (QCDSM), and Specific Measurable Achievable Relevant Timebase (SMART) method. Findings: This study found the cause of the partial set-up work being less than optimal due to problems with the Man, Method, and Machine factors. The results of this study show that the set-up time decreased from 240 minutes to 73.4 minutes or 68%. Meanwhile, the process cost for the partial set-up itself decreased from $75 to $3 or decreased by 97%. Research Limitation/implication: This research has limitations in the partial set-up process, which is less than optimal in plastic injection family-type moulds in the Motorcycle Industry. Originality/Value of paper: This research has value in optimising the production of plastic injection family-type moulds by reducing set-up time and operational costs for making spare parts in the Motorcycle Industry

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COVIDXception-Net: A Bayesian Optimization-Based Deep Learning Approach to Diagnose COVID-19 from X-Ray Images

Cited by 8 publications

References 52 publications

COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms

COVID-19 diagnosis on CT images with Bayes optimization-based deep neural networks and machine learning algorithms

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

Optimisation of the Partial Runner Set-up Process in Plastic Injection Moulds Using Eight Steps in the Motorcycle Industry

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