Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images

Hussein, Haval I.; Mohammed, Abdulhakeem; Hassan, Masoud Muhammed; Mstafa, Ramadhan J.

doi:10.1016/j.eswa.2023.119900

Cited by 21 publications

(2 citation statements)

References 47 publications

(53 reference statements)

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“…This could lead to a situation in which only well-resourced healthcare systems can take advantage of these technologies, exacerbating the existing inequalities in healthcare provision. Ensuring that the benefits of deep learning in healthcare are accessible to all, regardless of socioeconomic status, is an essential ethical consideration [ 60 – 62 ]. Addressing these challenges requires a multidisciplinary approach involving not only technologists but also ethicists, regulators, healthcare providers, and patients.…”

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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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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“…Deep learning-based methods, especially Convolutional Neural Networks (CNNs), have emerged as a new and effective solution to solve many computer vision-related problems, including agriculture [2,3], surveillance [4], healthcare [5,6], wildfire monitoring [7], and handwritten recognition [8]. In the last few years, several studies have targeted hot-rolled steel strip surface defect classification using different CNN architectures and training strategies.…”

Section: Introductionmentioning

confidence: 99%

CNN-based Hot-Rolled Steel Strip Surface Defects Classification: A Comparative Study Between Different Pre-Trained CNN Models

Bouguettaya¹,

Zarzour

2023

Preprint

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During the manufacturing process, hot-rolled steel strip surface defects occur frequently. These defects cause economic losses and risks in the use of these products. Therefore, it is crucial to develop automatic inspection systems to identify these defects. In the last few years, computer vision has emerged as an effective tool to identify these defects. Deep learning-based computer vision techniques, especially Convolutional Neural Networks (CNN), achieved state-of-the-art results for most computer vision tasks, including image classification. These results are obtained using a large amount of data. However, collecting such large datasets in the manufacturing field remains a challenging task. To overcome such a problem, a transfer learning-based framework with multiple CNN variants is proposed in this study. Therefore, different state-of-theart and widely used pre-trained CNN architectures, including VGG-16, VGG-19, ResNet50, ResNet50V2, InceptionV3, InceptionResNet-V2, MobileNet-V1, MobileNet-V2, MobileNet-V3 Small, and NASNetMobile, combined with transfer learning were investigated to evaluated their performances in classifying hot-rolled steel strips surface defects. The experimental results showed that MobileNet-V2 and InceptionResNetV2- based methods achieve better performance than all other models in terms of accuracy, loss, training and inference times, and model size.

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CNN-based hot-rolled steel strip surface defects classification: a comparative study between different pre-trained CNN models

Bouguettaya,

Zarzour

2024

Int J Adv Manuf Technol

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Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images

Cited by 21 publications

References 47 publications

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

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

CNN-based Hot-Rolled Steel Strip Surface Defects Classification: A Comparative Study Between Different Pre-Trained CNN Models

CNN-based hot-rolled steel strip surface defects classification: a comparative study between different pre-trained CNN models

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