Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models

Mahanty, Chandrakanta; Kumar, Raghvendra; Patro, S Gopal Krishna

doi:10.1007/s00354-022-00176-0

Cited by 18 publications

(5 citation statements)

References 44 publications

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“…In future, we can explore transfer learning to have good initial weights. In addition, changes to the model itself (ensembling [29,30], use of recent architectures [31][32][33]) can also be explored to improve the performance.…”

Section: Discussionmentioning

confidence: 99%

Wanet: weight and attention network for video summarization

Basu,

Pramanik,

Sarkar

2024

Discov Artif Intell

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In this paper, we propose a deep learning-based model, called Weight and Attention Network (WANet), for video summarization. The network comprises a simple multi-head attention mechanism, followed by a feed-forward network to obtain the frame importance scores. Summary keyshots are obtained from the scores using a combination of kernel temporal segmentation and the knapsack algorithm. Contrary to past methods, we first enrich the input frames with similar information as opposed to letting the model learn all the features by itself. A novel weight assignment mechanism is introduced to assign weights to the input frames based on their similarity before passing the same to the model. Experimental results on the SumMe and TVSum datasets indicate the effectiveness of the present method when compared to state-of-the-art methods applied to the same datasets.

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Section: Discussionmentioning

confidence: 99%

Wanet: weight and attention network for video summarization

Basu,

Pramanik,

Sarkar

2024

Discov Artif Intell

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“…concentrate on key elements by adjusting the channel weights. Numerous input/output sizes: MobileNetV2 is adaptable for a variety of applications thanks to its support for multiple input and output sizes [34]. While the MobileNet V2 is primarily concerned with reducing latency, it also gives small networks the ability to function effectively and accept input of any size.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

Detection of Pulmonary Embolism: Workflow Architecture and Comparative Analysis of the CNN Models

Tawte,

Gonge,

Joshi

et al. 2023

IJRITCC

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Machine learning has proven to be a practical medical image processing technique for pattern discovery in low-quality labelled and unlabeled datasets. Deep vein thrombosis and pulmonary embolism are both examples of venous thromboembolism, which is a key factor in patient mortality and necessitates prompt diagnosis by experts. An immediate diagnosis and course of treatment are necessary for the life-threatening cardiovascular condition known as pulmonary embolism (PE). In the study of medical imaging, especially the identification of PE, machine learning (ML) algorithms have produced encouraging results. This study's objective is to assess how well machine learning (ML) algorithms perform in identifying PE in computed tomography (CT) scans. A range of ML approaches were used to the dataset, including deep learning algorithms such as convolutional neural networks. The effectiveness of PE detection systems can be greatly enhanced by the use of cutting-edge methodologies like deep learning, which lowers the possibility of incorrect diagnoses and enables the quick administration of therapy to individuals who require it. This work contributes to the growing body of evidence that supports the use of ML in medical imaging and diagnosis. Future research should examine how these algorithms might be included into clinical workflows, resolving any potential implementation challenges, and making sure their adoption is done so in a secure and efficient way. In this study, we provide a thorough evaluation of three different models: the streamlined architecture MobileNetV2 with an accuracy of 96%, compared to other models like the Xception model with an accuracy of 91%, and the Efficientnet B5 model with an accuracy of 97%, after observation and process following.

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“…The topics of our selected papers are many fold. While many contributions focus on image processing and detection of COVID-19 [ 3 , 8 , 11 , 14 ], there is also a shift to multi-disease prediction [ 10 ] and predicting post-COVID-19 complications [ 5 ], which have not been addressed in research in the early days of COVID-19.…”

Section: Selected Papersmentioning

confidence: 99%

“…Hence, this special issue has been divided into two parts, where Part 1 was already published in 2021 [ 7 ]. Part 2 with 13 articles [ 1 – 6 , 8 – 14 ] is introduced in this editorial. Later publications about COVID-19 will be included in the regular issues of the New Generation Computing journal.…”

Section: Introductionmentioning

confidence: 99%

“…-Effects of COVID-19 Confinements -Assessing Countries' readiness for coping with epidemics -Studies related to animal originated diseases -Challenges in battling with epidemics -AI for forecasting -AI and detection -Generating recommendations -AI in genome sequencing -AI-assisted testing -Role of AI in contact tracing -Situation awareness -Computational drug repurposing The topics of our selected papers are many fold. While many contributions focus on image processing and detection of COVID-19 [3,8,11,14], there is also a shift to multi-disease prediction [10] and predicting post-COVID-19 complications [5], which have not been addressed in research in the early days of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

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