Results 131 to 140 of about 73,468 (309)

An Extensive Study of Convolutional Neural Networks: Applications in Computer Vision for Improved Robotics Perceptions

Sensors
Convolutional neural networks (CNNs), a type of artificial neural network (ANN) in the deep learning (DL) domain, have gained popularity in several computer vision applications and are attracting research in other fields, including robotic perception ...
Ravi Raj, Andrzej Kos
doaj   +1 more source

Performance analysis of seven Convolutional Neural Networks (CNNs) with transfer learning for Invasive Ductal Carcinoma (IDC) grading in breast histopathological images. [PDF]

Sci Rep, 2022
Voon W, Hum YC, Tee YK, Yap WS, Salim MIM, Tan TS, Mokayed H, Lai KW.   +7 more
europepmc   +1 more source

PancDS in Real‐World Practice: A Prospective Multicenter Validation of a Clinical Decision‐Support System Bridging Experience Gaps in Pancreatic Lesion Diagnosis

Advanced Science, EarlyView.
A biomimetic artificial intelligence system, PancDS, has been developed to distinguish pancreatic ductal adenocarcinoma from mass‐forming pancreatitis by adaptively integrating clinical data, radiomics, and deep learning features. Validated across multicenter, reader‐study, and prospective settings, PancDS improves diagnostic accuracy, particularly for
Zhibo Wang, Weinuo Qu, Wenwen Cai, Chuhuai Wang, Chenxi Lyu, Qingguo Xie, Qian Chu, Yaqi Shen, Peng Xiao, Feng Li, Qingpeng Zhang, Jiali Li, Jeong Min Lee, Zhen Li   +13 more
wiley   +1 more source

The Application of Convolutional Neural Networks (CNNs) to Recognize Defects in 3D-Printed Parts. [PDF]

Materials (Basel), 2021
Wen H, Huang C, Guo S.
europepmc   +1 more source

Food Image Recognition by Using Convolutional Neural Networks (CNNs)

CoRR, 2016
Food image recognition is one of the promising applications of visual object recognition in computer vision. In this study, a small-scale dataset consisting of 5822 images of ten categories and a five-layer CNN was constructed to recognize these images.
openaire   +2 more sources

PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes

Advanced Science, EarlyView.
A plant genomic foundation model pre‐trained on 12 species enables both accurate gene prediction and de novo gene design. Through AI‐human knowledge screening, seven designed sequences showed transcriptional activity in plants, with two expressing stable proteins—demonstrating the first DNA‐RNA‐protein expression of LLM‐generated genes in plants and ...
Changhao Li, Qizhe Zhang, Hanchen Chen, Kepeng Lin, Chengfang Luo, Mengying Yang, Wei Xu, Fan Yao, Jianbing Yan, Qing‐Yong Yang, Xuehai Hu   +10 more
wiley   +1 more source

Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved generalization and fast adaptation

Scientific Reports
Deep neural networks can improve the quality of fluorescence microscopy images. Previous methods, based on Convolutional Neural Networks (CNNs), require time-consuming training of individual models for each experiment, impairing their applicability and ...
Azaan Rehman, Alexander Zhovmer, Ryo Sato, Yoh-suke Mukouyama, Jiji Chen, Alberto Rissone, Rosa Puertollano, Jiamin Liu, Harshad D. Vishwasrao, Hari Shroff, Christian A. Combs, Hui Xue   +11 more
doaj   +1 more source

Effectiveness of Learning Systems from Common Image File Types to Detect Osteosarcoma Based on Convolutional Neural Networks (CNNs) Models. [PDF]

J Imaging, 2021
Loraksa C, Mongkolsomlit S, Nimsuk N, Uscharapong M, Kiatisevi P.   +4 more
europepmc   +1 more source

Bias‐Tunable Two‐Terminal Organic Photodetector for Intelligent Imaging

Advanced Science, EarlyView.
A vertically stacked two‐terminal photodetector with a symmetric donor–acceptor–donor trilayer active exhibits reconfigurable photoresponse with bias‐tunable magnitude and polarity, together with sub‐millisecond response speed and a wide tunable output window.
Sangin Hahn, Sanghoon Park, Seunghyup Yoo   +2 more
wiley   +1 more source

A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP). [PDF]

Transl Vis Sci Technol, 2021
Wang YZ, Wu W, Birch DG.
europepmc   +1 more source