Deep transfer learning for COVID-19 detection and infection localization with superpixel based segmentation.
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In this article, the authors proposed a deep learning based framework to enhance the diagnostic values of chest X-ray images for improved clinical outcomes, which is realized as a variant of the conventional SqueezeNet classifier with segmentation capabilities.About:
This article is published in Sustainable Cities and Society.The article was published on 2021-08-16 and is currently open access. It has received 27 citations till now.read more
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Journal ArticleDOI
Next-generation energy systems for sustainable smart cities: Roles of transfer learning
Yassine Himeur,Mariam Ahmed Elnour,Fodil Fadli,Nader Meskin,Ioan Petri,Yacine Rezgui,Faycal Bensaali,Abbes Amira +7 more
TL;DR: In this article , transfer learning has been proposed as a promising solution to alleviate the issues of traditional machine learning algorithms, such as they might not perform as expected, take much time in training, or do not have enough input data to generalize well.
Journal ArticleDOI
Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification
TL;DR: In this article , a multistage superpixel classification-based disease localization and severity assessment framework is proposed to generate a compact disease boundary, infection map, and grade the infection severity.
Journal ArticleDOI
MXT: A New Variant of Pyramid Vision Transformer for Multi-label Chest X-ray Image Classification
TL;DR: A new variant of pyramid vision Transformer for multi-label chest X-ray image classification, named MXT, which can capture both short and long-range visual information through self-attention and can assist radiologists in diagnoses of lung diseases and check the placement of catheters, which could reduce the work pressure of medical staff.
Journal ArticleDOI
Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review
Jaspreet Kaur,Prabhpreet Kaur +1 more
TL;DR: In this article, the authors discuss the outline of the deep learning techniques with medical imaging such as outburst prediction, virus transmitted indications, detection and treatment aspects, vaccine availability with remedy research, and problems faced by the radiologists during medical imaging techniques and deep learning approaches for diagnosing the COVID-19 infections.
Journal ArticleDOI
Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function
Faizal Hajamohideen,Noushath Shaffi,Mufti Mahmud,Karthikeyan Subramanian,Arwa Al Sariri,Viswan Vimbi,Abdelhamid Abdesselam +6 more
TL;DR: In this paper , a Siamese convolutional neural network (SCNN) architecture was proposed for the representation of input MRI images as k-dimensional embeddings, which were subsequently used for the 4-way classification of Alzheimer's disease.
References
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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
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