SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot Recognition

Wang, Shuihua; Wu, Kai-Hong; Chu, Tianshu; Fernandes, Steven Lawrence; Zhou, Qinghua; Zhang, Yudong

doi:10.1155/2021/5792975

Cited by 13 publications

(6 citation statements)

References 33 publications

(31 reference statements)

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“…The results of deep learning [20] rely on data, and a large amount of simulation data provides the possibility of applying deep learning to image denoising. This is especially true for images with complex backgrounds, where traditional algorithms often do not yield good results even after a lot of effort [21].…”

Section: Application Of Cnn In Image Denoisingmentioning

confidence: 99%

A Review of Convolutional Neural Network based Image Denoising Algorithms

Wang

2023

EAI Endorsed Trans e-Learn

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Currently, image-denoising algorithms based on convolutional neural networks (CNN)have been widely used and have achieved good results. Compared with traditional image-denoisingmethods, it has powerful learning ability and efficient algorithms. This paper summarizes traditionaldenoising methods and CNN-based image denoising methods, and introduces the basics of imagedenoising in detail, which is helpful for readers who are starting with image denoising processing.In addition, this paper also summarizes some commonly used datasets in the field of imageprocessing, which makes it easier for us to denoise images. Finally, some suggestions for improvingthe performance of CNN image denoising are presented, and possible future research directions arediscussed.

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Section: Application Of Cnn In Image Denoisingmentioning

confidence: 99%

A Review of Convolutional Neural Network based Image Denoising Algorithms

Wang

2023

EAI Endorsed Trans e-Learn

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“…They can do an excellent job of image extraction. It has been found that they can be used not only for image classification tasks but also for backbone architectures in more complex object detection tasks [43][44][45][46][47].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Review on One-Stage Object Detection Based on Deep Learning

Zhang¹,

Cloutier²

2022

EAI Endorsed Transactions on e-Learning

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As a popular research direction in computer vision, deep learning technology has promoted breakthroughs in the field of object detection. In recent years, the combination of object detection and the Internet of Things (IoT) has been widely used in the fields of face recognition, pedestrian detection, unmanned driving, and customs detection. With the development of object detection, two different detection algorithms, one-stage, and two-stage have gradually formed. This paper mainly introduces the one-stage object detection algorithm. Firstly, the development process of the convolutional neural network is briefly reviewed, Then, the current mainstream one-stage object detection model is summarized. Based on YOLOv1, it is continuously optimized, and the improvements and shortcomings are summarized in detail. Finally, a summary is made based on the difficulties and challenges of one-stage object detection algorithms.

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“…L. Fernandes & Bala, 2016;S. L. Fernandes & Jha, 2020;ORTIZ et al, 2021;Wang et al, 2021). Quantum machine learning-based diagnosis systems are also gaining increased intention (Umer et al, n.d.).…”

Section: Quantum Computing Conceptsmentioning

confidence: 99%

A Comprehensive Survey on Quantum Machine Learning and Possible Applications

Umer

Sharif

2022

International Journal of E-Health and Medical Communications

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Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.

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SOSPCNN: Structurally Optimized Stochastic Pooling Convolutional Neural Network for Tetralogy of Fallot Recognition

Cited by 13 publications

References 33 publications

A Review of Convolutional Neural Network based Image Denoising Algorithms

A Review of Convolutional Neural Network based Image Denoising Algorithms

Review on One-Stage Object Detection Based on Deep Learning

A Comprehensive Survey on Quantum Machine Learning and Possible Applications

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