UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection

Abdar, Moloud; Salari, Soorena; Qahremani, Sina; Lam, Hak‐Keung; Karray, Fakhri; Hussain, Sadiq; Khosravi, Abbas; Acharya, U. Rajendra; Makarenkov, Vladimir; Nahavandi, Saeid

doi:10.1016/j.inffus.2022.09.023

Cited by 67 publications

(24 citation statements)

References 71 publications

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“…These features were then fused and used as inputs to the K-NN classifier reaching 96.6% accuracy. On the other hand, others fused-only deep features like [ 50 ] where a straightforward but effective DL feature fusion model built on two customized CNNs was suggested for diagnosing COVID-19. Also, the authors of the research article [ 51 ] used 13 CNN models to diagnose COVID-19 from X-ray images.…”

Section: Related Work On Covid-19 Diagnosismentioning

confidence: 99%

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

Attallah

2023

Chemometrics and Intelligent Laboratory Systems

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Section: Related Work On Covid-19 Diagnosismentioning

confidence: 99%

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

Attallah

2023

Chemometrics and Intelligent Laboratory Systems

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“…Abdar, Salari, et al, 2021 developed a new direct and crossed based binary feature called BARF with integrated uncertainty aware module for the automatic classification of medical images. The study used the Monte Carlo (MC) dropout method during inference to obtain the mean and standard deviation of the predictions.…”

Section: Related Workmentioning

confidence: 99%

Effective deep learning based multimodal sentiment analysis from unstructured big data

2022

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More recently, as images, memes and graphics interchange formats have dominated social feeds, typographic/infographic visual content has emerged as an important social media component. This multimodal text combines text and image, defining a novel visual language that must be analysed because it has the potential to modify, confirm or grade the sentiment's polarity. The problem is how to effectively use information from the visual and textual content in image-text posts. This article presents a new deep learning-based multimodal sentiment analysis (MSA) model using multimodal data such as images, text and multimodal text (image with embedded text). The text analytic unit, the discretization control unit, the picture analytic component and the decision-making component are all included in this system. The discretization unit separates the text from the picture using the variant and channel augmented maximally stable extremal regions (VCA-MSERs) technique, which are then analysed as discrete elements and fed into the appropriate image and text analytics units. The text analytics system utilizes a stacked recurrent neural network with multilevel attention and feedback module (SRNN-MAFM) to detect the sentiment of the text. A deep convolutional neural network (CNN) structure with parallel-dilated convolution and self-attention module (PDC-SAM) is developed to forecast the emotional response to visual content. Finally, the decision component employs a Boolean framework including an OR function to evaluate and classify the output into three fine-grained sentiment classes: positive, neutral and negative. The proposed work is simulated in the python platform using the STS-Gold, Flickr 8k and B-T4SA datasets for sentiment analysis of text and visual and multimodal text. Simulation outcomes proved that the suggested method achieved better accuracy of 97.8%, 97.7% and 90% for text, visual and MSA individually compared to other methods.

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“…Nowadays different DL‐based approaches are presented in the medical field for the diagnosis of several diseases from images like brain tumor detection, 5 eye disease identification, 6 lung cancer, 7 skin cancer detection, 8 etc. The capability of DL‐based approaches in quantifying uncertainty in medical case studies has enabled them to better recognize the diseased areas of the human body 9–16 . In DL, a convolutional neural network (CNN) is utilized for feature extraction from images and gives a better solution for melanoma regions from skin images.…”

Section: Introductionmentioning

confidence: 99%

Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network

Nawaz

Nazir

Masood

et al. 2022

Int J Imaging Syst Tech

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Melanoma is the most fatal type of skin cancer which can cause the death of victims at the advanced stage. Extensive work has been presented by the researcher on computer vision for skin lesion localization. However, correct and effective melanoma segmentation is still a tough job because of the extensive variations found in the shape, color, and sizes of skin moles. Moreover, the presence of light and brightness variations further complicates the segmentation task. We have presented improved deep learning (DL)-based approach, namely, the DenseNet77-based UNET model. More clearly, we have introduced the DenseNet77 network at the encoder unit of the UNET approach to computing the more representative set of image features. The calculated keypoints are later segmented by the decoder of the UNET model. We have used two standard datasets, namely, the ISIC-2017 and ISIC-2018 to evaluate the performance of the proposed approach and acquired the segmentation accuracies of 99.21% and 99.51% for the ISIC-2017 and ISIC-2018 datasets, respectively. We have confirmed through both the quantitative and qualitative results that the proposed improved UNET approach is robust to skin lesions segmentation and can accurately recognize the moles of varying colors and sizes.

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UncertaintyFuseNet: Robust uncertainty-aware hierarchical feature fusion model with Ensemble Monte Carlo Dropout for COVID-19 detection

Cited by 67 publications

References 71 publications

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics

Effective deep learning based multimodal sentiment analysis from unstructured big data

Melanoma segmentation: A framework of improved DenseNet77 and UNET convolutional neural network

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