Fine-tuned convolutional neural network for different cardiac view classification

Kumar, Basant; Haq, Mohd Anul; Sreenivasulu, P.; Siva, D.; Alazzam, Malik Bader; Alassery, Fawaz; Karupusamy, Sathishkumar

doi:10.1007/s11227-022-04587-0

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…Besides, the title called deep-learning methods include both Machine learning (ML) and Deep learning (DL) models that have been broadly implemented in many areas such as image fusion [27,28], agricultural surveillance [29,30], environmental monitoring [31][32][33][34][35][36], sentiment analyses [37], medical image processing [38], and cyber security [39][40][41]. Moreover, the implementation of the deep learning methods can be divided into (1) training models, and (2) non-training models.…”

Section: Introductionmentioning

confidence: 99%

ICIF: Image fusion via information clustering and image features

et al. 2023

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Image fusion technology is employed to integrate images collected by utilizing different types of sensors into the same image to generate high-definition images and extract more comprehensive information. However, all available techniques derive the features of the images by utilizing each sensor separately, resulting in poorly correlated image features when different types of sensors are utilized during the fusion process. The fusion strategy to make up for the differences between features alone is an important reason for the poor clarity of fusion results. Therefore, this paper proposes a fusion method via information clustering and image features (ICIF). First, the weighted median filter algorithm is adopted in the spatial domain to realize the clustering of images, which uses the texture features of an infrared image as the weight to influence the clustering results of the visible light image. Then, the image is decomposed into the base layer, bright detail layer, and dark detail layer, which improves the correlations between the layers after conducting the decomposition of a source graph. Finally, the characteristics of the images collected by utilizing sensors and feature information between the image layers are used as the weight reference of the fusion strategy. Hence, the fusion images are reconstructed according to the principle of extended texture details. Experiments on public datasets demonstrate the superiority of the proposed strategy over state-of-the-art methods. The proposed ICIF highlighted targets and abundant details as well. Moreover, we also generalize the proposed ICIF to fuse images with different sensors, e.g., medical images and multi-focus images.

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Section: Introductionmentioning

confidence: 99%

ICIF: Image fusion via information clustering and image features

et al. 2023

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“…Nowadays, scientists and researchers use machine-learning (ML) and deep-learning (DL) models in several applications, including agriculture ( 4 , 5 ), environment ( 6 – 12 ), text sentiment analyses ( 13 ), medicine ( 14 ), and cyber security ( 15 – 17 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting oxygen requirements in patients with coronavirus disease 2019 using an artificial intelligence-clinician model based on local non-image data

et al. 2022

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BackgroundWhen facing unprecedented emergencies such as the coronavirus disease 2019 (COVID-19) pandemic, a predictive artificial intelligence (AI) model with real-time customized designs can be helpful for clinical decision-making support in constantly changing environments. We created models and compared the performance of AI in collaboration with a clinician and that of AI alone to predict the need for supplemental oxygen based on local, non-image data of patients with COVID-19.Materials and methodsWe enrolled 30 patients with COVID-19 who were aged >60 years on admission and not treated with oxygen therapy between December 1, 2020 and January 4, 2021 in this 50-bed, single-center retrospective cohort study. The outcome was requirement for oxygen after admission.ResultsThe model performance to predict the need for oxygen by AI in collaboration with a clinician was better than that by AI alone. Sodium chloride difference >33.5 emerged as a novel indicator to predict the need for oxygen in patients with COVID-19. To prevent severe COVID-19 in older patients, dehydration compensation may be considered in pre-hospitalization care.ConclusionIn clinical practice, our approach enables the building of a better predictive model with prompt clinician feedback even in new scenarios. These can be applied not only to current and future pandemic situations but also to other diseases within the healthcare system.

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Adaptive robust control algorithm for enhanced path-tracking performance of automated driving in critical scenarios

Taghavifar

Shojaei

2023

Soft Comput

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Fine-tuned convolutional neural network for different cardiac view classification

Cited by 8 publications

References 32 publications

ICIF: Image fusion via information clustering and image features

ICIF: Image fusion via information clustering and image features

Predicting oxygen requirements in patients with coronavirus disease 2019 using an artificial intelligence-clinician model based on local non-image data

Adaptive robust control algorithm for enhanced path-tracking performance of automated driving in critical scenarios

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