Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Shoeibi, Afshin; Khodatars, Marjane; Jafari, Mahboobeh; Ghassemi, Navid; Sadeghi, Delaram; Moridian, Parisa; Khadem, Ali; Alizadehsani, Roohallah; Hussain, Sadiq; Zare, Assef; Sani, Zahra Alizadeh; Khozeimeh, Fahime; Nahavandi, Saeid; Acharya, U. Rajendra; Gorriz, Juan M.

doi:10.1016/j.neucom.2024.127317

Cited by 13 publications

(4 citation statements)

References 372 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This solved the spatial discontinuity problem in image segmentation and allowed for successful pixel-level image classification, which laid the groundwork for deep learning image segmentation ( Poulakis and Westman, 2021 ). For medical image segmentation tasks, considering the insufficient feature map recovery and lack of spatial consistency of FCNs, Shoeibi et al (2024) proposed a variant network of FCN called U-Net. Understanding of the brain networks that control appetite is developing quickly thanks to developments in neuro-technology for mapping, modifying, and tracking molecularly defined cell types.…”

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model

Byeon,

Al-Kubaisi,

Dutta

et al. 2024

Front. Comput. Neurosci.

View full text Add to dashboard Cite

According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) and a Mel-spectrogram are the basis of this cough recognition technique. First, we combine the voice in a variety of intricate settings and improve the audio data. After that, we preprocess the data to make sure its length is consistent and create a Mel-spectrogram out of it. A novel model for brain tumor segmentation (BTS), Intelligence Cascade U-Net (ICU-Net), is proposed to address these issues. It is built on dynamic convolution and uses a non-local attention mechanism. In order to reconstruct more detailed spatial information on brain tumours, the principal design is a two-stage cascade of 3DU-Net. The paper’s objective is to identify the best learnable parameters that will maximize the likelihood of the data. After the network’s ability to gather long-distance dependencies for AI, Expectation–Maximization is applied to the cascade network’s lateral connections, enabling it to leverage contextual data more effectively. Lastly, to enhance the network’s ability to capture local characteristics, dynamic convolutions with local adaptive capabilities are used in place of the cascade network’s standard convolutions. We compared our results to those of other typical methods and ran extensive testing utilising the publicly available BraTS 2019/2020 datasets. The suggested method performs well on tasks involving BTS, according to the experimental data. The Dice scores for tumor core (TC), complete tumor, and enhanced tumor segmentation BraTS 2019/2020 validation sets are 0.897/0.903, 0.826/0.828, and 0.781/0.786, respectively, indicating high performance in BTS.

show abstract

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model

Byeon,

Al-Kubaisi,

Dutta

et al. 2024

Front. Comput. Neurosci.

View full text Add to dashboard Cite

show abstract

“…These restrictions are risky for people with COVID-19. Therefore, medical imaging tests are performed first to initially detect COVID-19, followed by RT-PCR tests to help doctors make an accurate final diagnosis [ 4 ]. Computed Tomography (CT) and Chest X-ray (CXR) are two common medical imaging techniques used for COVID-19 detection, each with unique advantages [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, it is important to note that CT scans, compared to CXRs, require a higher radiation dosage, potentially increasing the risk of radiation exposure to patients. And, for patients, CT images are far more expensive than CXR images [ 4 ]. In contrast, Chest X-ray (CXR) exhibits advantages such as low radiation exposure, rapid acquisition, and cost-effectiveness.…”

Section: Introductionmentioning

confidence: 99%

A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection

Wang,

Ren,

Guo

2024

PLoS ONE

View full text Add to dashboard Cite

The Coronavirus Disease 2019(COVID-19) has caused widespread and significant harm globally. In order to address the urgent demand for a rapid and reliable diagnostic approach to mitigate transmission, the application of deep learning stands as a viable solution. The impracticality of many existing models is attributed to excessively large parameters, significantly limiting their utility. Additionally, the classification accuracy of the model with few parameters falls short of desirable levels. Motivated by this observation, the present study employs the lightweight network MobileNetV3 as the underlying architecture. This paper incorporates the dense block to capture intricate spatial information in images, as well as the transition layer designed to reduce the size and channel number of the feature map. Furthermore, this paper employs label smoothing loss to address the inter-class similarity effects and uses class weighting to tackle the problem of data imbalance. Additionally, this study applies the pruning technique to eliminate unnecessary structures and further reduce the number of parameters. As a result, this improved model achieves an impressive 98.71% accuracy on an openly accessible database, while utilizing only 5.94 million parameters. Compared to the previous method, this maximum improvement reaches 5.41%. Moreover, this research successfully reduces the parameter count by up to 24 times, showcasing the efficacy of our approach. This demonstrates the significant benefits in regions with limited availability of medical resources.

show abstract

“…Firstly, they eliminate the need for time-consuming questionnaires by gathering data directly from students' activities within the system [5]. Additionally, unlike static results from questionnaires, the learning styles identified through these automated approaches are dynamic; they can adapt and change in response to shifts in students' behaviors, ensuring a more accurate and personalized learning experience; such a type of approach is known as an implicit approach [6]. The implicit approach is much better than the explicit approach, but it may face a cold-start problem when new users log into the system.…”

Section: Introductionmentioning

confidence: 99%

Enhancing E-Learning Adaptability with Automated Learning Style Identification and Sentiment Analysis: A Hybrid Deep Learning Approach for Smart Education

Hussain,

Yu,

Asim

et al. 2024

Information

View full text Add to dashboard Cite

In smart education, adaptive e-learning systems personalize the educational process by tailoring it to individual learning styles. Traditionally, identifying these styles relies on learners completing surveys and questionnaires, which can be tedious and may not reflect their true preferences. Additionally, this approach assumes that learning styles are fixed, leading to a cold-start problem when automatically identifying styles based on e-learning platform behaviors. To address these challenges, we propose a novel approach that annotates unlabeled student feedback using multi-layer topic modeling and implements the Felder–Silverman Learning Style Model (FSLSM) to identify learning styles automatically. Our method involves learners answering four FSLSM-based questions upon logging into the e-learning platform and providing personal information like age, gender, and cognitive characteristics, which are weighted using fuzzy logic. We then analyze learners’ behaviors and activities using web usage mining techniques, classifying their learning sequences into specific styles with an advanced deep learning model. Additionally, we analyze textual feedback using latent Dirichlet allocation (LDA) for sentiment analysis to enhance the learning experience further. The experimental results demonstrate that our approach outperforms existing models in accurately detecting learning styles and improves the overall quality of personalized content delivery.

show abstract

Automated detection and forecasting of COVID-19 using deep learning techniques: A review

Cited by 13 publications

References 372 publications

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model

A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection

Enhancing E-Learning Adaptability with Automated Learning Style Identification and Sentiment Analysis: A Hybrid Deep Learning Approach for Smart Education

Contact Info

Product

Resources

About