Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new *** using DL,the extraction of advanced data representations and knowledge can be made *** effective DL techniques help to f...
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Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new *** using DL,the extraction of advanced data representations and knowledge can be made *** effective DL techniques help to find more hidden *** learning has a promising future due to its great performance and *** need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively.A survey on DL ways,advantages,drawbacks,architectures,and methods to have a straightforward and clear understanding of it from different views is explained in the ***,the existing related methods are compared with each other,and the application of DL is described in some applications,such as medical image analysis,handwriting recognition,and so on.
The integration of advanced security technologies, particularly the widespread deployment of mobile cameras equipped with real-time face recognition capabilities, heralds a new era of surveillance capabilities. This p...
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In the rapidly evolving landscape of technology, the need for swift and efficient deployment of software and applications has never been more critical. Due to the rapid and constant evolution of innovation, there is a...
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One of the quintessential examples in ophthalmology is the early detection of glaucoma, a disease that accounts for significant irrecoverable vision loss when caught later. Conventional diagnostic methods are based on...
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In recommendation systems, collaborative filtering algorithms play a vital role in offering personalized recommendations based on user-item interactions. This paper conducts a detailed comparative study of three colla...
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Crowd management is a cumbersome task that requires broad analysis and grasp of various constraints. The main hurdles include security issues, unexpected crowd dynamics over which there is typically minimal or no cont...
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Coronary artery disease (CAD) is the primary cause of mortality and a key driver of healthcare expenses globally. Accurately segmenting stenotic regions from coronary angiograms is decisive in identifying and treating...
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ISBN:
(纸本)9798350389609
Coronary artery disease (CAD) is the primary cause of mortality and a key driver of healthcare expenses globally. Accurately segmenting stenotic regions from coronary angiograms is decisive in identifying and treating cardiovascular diseases. However, it is a challenging task for medical professionals to use X-ray Coronary Angiogram (XCA) due to the reduced signal quality, the existence of obstructive contextual elements, and various types of noise. Furthermore, handcrafted segmentation is arduous, laborious, and prone to inconsistencies and human errors. In this context, this research aim to develop an automatic stenosis segmentation system using a deep network. Initially, the input image is processed by Gaussian filters and the improved angiogram is filtered by Hessian-based Vessel Filtering (HVF) technique to increase the clarity of vascular components in the angiogram images. This study identifies the branch points (BP) in the angiograms based on the eigenvalues of the Hessian matrix. The proposed model employ a Mask Region-based Convolutional Neural Network (Mask R-CNN) to provide precise pixel-wise masks for every detected stenosis. The proposed Mask R-CNN includes (i) ResNet50 as the backbone network to extract significant attributes;(ii) Region Proposal Network (RPN) to identify possible Regions of Interest (RoIs) that may have stenosis;(iii) RoI Align to ensure precise alignment of the RoIs for improved mask prediction;and (iv) a mask branch to create a pixel-level segmentation mask for each RoI. The effectiveness of the model is assessed by applying an open-access ARCADE Phase 1 (Automatic Region-based CAD diagnostics using XCA images) dataset. The Mask R-CNN model achieves better results with 97.8% dice score, 92.9% sensitivity, and 96.6% specificity. Besides, it provides reduced standard deviation (SD) in the segmentation task with a 0.8% dice score, 1.0% sensitivity, and 1.0% specificity. These results shows that the Mask R-CNN model provides more relia
Lung cancer stands as a formidable and prevalent threat, necessitating urgent attention to early diagnosis and precise treatment to mitigate its high fatality rates. In this context, the utilization of computed tomogr...
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IoT is an acronym for the Internet of Things, and it refers to the interconnectivity of everyday objects, including home appliances, wearable devices, vehicles, and industrial equipment, through the Internet. IoT enab...
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In the present world a lot of data is generated via twitter, Instagram, WhatsApp etc. in different languages. It is important and necessary task to detect the offensive language among those data to create healthy and ...
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