Results 41 to 50 of about 6,547,057 (287)
Deep segmentation networks predict survival of non-small cell lung cancer [PDF]
, 2019 Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography-computed
Allen, Bryan +16 more
core +2 more sources
International Journal of Digital Earth, 2023 Accurate landslide extraction is significant for landslide disaster prevention and control. Remote sensing images have been widely used in landslide investigation, and landslide extraction methods based on deep learning combined with remote sensing ...
Hesheng Chen +7 more
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
Exploring the U-Net++ Model for Automatic Brain Tumor Segmentation
IEEE Access, 2021 The accessibility and potential of deep learning techniques have increased considerably over the past years. Image segmentation is one of the many fields which have seen novel implementations being developed to solve problems in the domain.
Neil Micallef +2 more
doaj +1 more source
, 2022 In neural networks, the property of being equivariant to transformations improves generalization when the corresponding symmetry is present in the data. In particular, scale-equivariant networks are suited to computer vision tasks where the same classes of objects appear at different scales, like in most semantic segmentation tasks.
Sangalli, Mateus +3 more
openaire +3 more sources
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
doaj +1 more source
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
doaj +1 more source
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
openaire +2 more sources
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
doaj +1 more source