Results 31 to 40 of about 295,660 (323)
CROP AND WEED SEGMENTATION ON GROUND-BASED IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORK [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023 Weed management is of crucial importance in precision agriculture to improve productivity and reduce herbicide pollution. In this regard, showing promising results, deep learning algorithms have increasingly gained attention for crop and weed ...
H. Fathipoor, R. Shah-Hosseini, H. Arefi
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International Journal of Applied Earth Observations and Geoinformation, 2023 Forest Change Detection (FCD) is a critical component of natural resource monitoring and conservation strategies, enabling informed decision-making. Various methods utilizing the power of artificial intelligence (AI) have been developed for detecting and
Kassim Kalinaki +2 more
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DRINet for medical image segmentation [PDF]
, 2018 Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The UNet architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many ...
Bentley, P +5 more
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Segmentation of skin cancer using Fuzzy U-network via deep learning
Measurement: Sensors, 2023 The most common cancer in the world is skin cancer. In recent years, one of the most important challenges to public health has been melanoma, the most dangerous type of skin cancer. In this paper, a novel MFO-Fuzzy U net has been proposed to segmentation
A. Bindhu, K.K. Thanammal
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GAU U-Net for multiple sclerosis segmentation
Alexandria Engineering Journal, 2023 Multiple sclerosis is an auto immune disease which affects the brain and nervous system. A total of 2.8 million people are estimated to live with Multiple sclerosis worldwide (35.9 per 100,000 population).
Roba Gamal, Hoda Barka, Mayada Hadhoud
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Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling [PDF]
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020 Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the Temporal Convolutional Network (TCN) have shown good results in a variety of sequence modelling tasks. While their receptive field grows exponentially with the number of layers, computing the convolutions over very long sequences of features in each layer is time and ...
Stoller, Daniel +3 more
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Road Extraction by Deep Residual U-Net
, 2017 Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network which combines the strengths of residual learning and U-Net is proposed for road area ...
Liu, Qingjie +2 more
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Clinically Interpretable Nuclei Segmentation for Robust Histopathological Image Analysis
Applied SciencesBackground/Objectives: Accurate nuclear segmentation is a fundamental step in computational pathology, enabling reliable estimation of cellularity and nuclear morphology. However, segmentation models are typically evaluated under ideal imaging conditions,
Liana Stanescu, Cosmin Stoica Spahiu
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U-Net: Convolutional Networks for Biomedical Image Segmentation
, 2015 There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available ...
Brox, Thomas +2 more
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Fully automated condyle segmentation using 3D convolutional neural networks
Scientific Reports, 2022 The aim of this study was to develop an auto-segmentation algorithm for mandibular condyle using the 3D U-Net and perform a stress test to determine the optimal dataset size for achieving clinically acceptable accuracy.
Nayansi Jha +6 more
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