Fully Convolutional Neural Network with Attention Module for Semantic Segmentation [PDF]
Jisuanji kexue yu tansuo, 2022 A fully convolutional neural network is a powerful end-to-end model that is widely used in the field of semantic segmentation and has achieved great success.
OU Yangliu, HE Xi, QU Shaojun
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Segmentation of retinal fluid based on deep learning: application of three-dimensional fully convolutional neural networks in optical coherence tomography images [PDF]
International Journal of Ophthalmology, 2019 AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid. METHODS: A two-dimensional (2D) fully convolutional network for retinal segmentation was employed.
Meng-Xiao Li +6 more
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Automatic Segmentation of Prostate Cancer using cascaded Fully Convolutional Network [PDF]
E3S Web of Conferences, 2021 In this paper we proposed a prostate segmentation and also tumour detection using deep neural networks. The cutting-edge deep learning techniques are useful compared to the challenges of machine learning based feature extraction techniques.
Kora Padmavathi +6 more
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Bone tumor examination based on FCNN-4s and CRF fine segmentation fusion algorithm
Journal of Bone Oncology, 2023 Background and objective: Bone tumor is a kind of harmful orthopedic disease, there are benign and malignant points. Aiming at the problem that the accuracy of the existing machine learning algorithm for bone tumor image segmentation is not high, a bone ...
Shiqiang Wu +6 more
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Distributions of various compositions in granite specimen using fully convolutional network
Zhongguo dizhi zaihai yu fangzhi xuebao, 2021 The distributions of various compositions are the fundamental issues in studying the physical and mechanical properties of rock material. In this study, fully convolutional network (FCN) and the video images photographed during the uniaxial compression ...
Chuxiong ZHU +2 more
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Fully Convolutional Neural Network Structure and Its Loss Function for Image Classification
IEEE Access, 2022 The overall structure of a convolutional neural network classifier includes multiple convolutional layers and one or more linear layers. Due to the fully connected characteristics of linear layer networks, there are usually many parameters, which may ...
Qiuyu Zhu, Xuewen Zu
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FULLY CONVOLUTIONAL NETWORK BASED SHADOW EXTRACTION FROM GF-2 IMAGERY [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2018 There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building ...
Z. Li +5 more
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Background Subtraction Using Multiscale Fully Convolutional Network
IEEE Access, 2018 Background modeling and subtraction based on change detection are the first step in many high-level computer vision applications. Many background subtraction methods have been proposed in the recent past and their efforts mainly focus on two aspects ...
Dongdong Zeng, Ming Zhu
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Tire Defect Detection Using Fully Convolutional Network
IEEE Access, 2019 A deep convolutional neural network has recently witnessed rapid progress due to the strong feature learning capability. In this paper, we focus on its application in the industrial field and propose a method based on a fully convolutional network (FCN ...
Ren Wang +3 more
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Fully Symmetric Convolutional Network for Effective Image Denoising
Applied Sciences, 2019 Neural-network-based image denoising is one of the promising approaches to deal with problems in image processing. In this work, a deep fully symmetric convolutional⁻deconvolutional neural network (FSCN) is proposed for image denoising.
Steffi Agino Priyanka, Yuan-Kai Wang
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