Results 31 to 40 of about 17,494 (194)
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
, 2017 We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs).
Lin, Mengxiao +3 more
core +1 more source
End-to-end Keyword Spotting using Xception-1d
ESANN 2021 proceedings, 2021 In proceedings of ESANN 2021 conference.
Vallés-Pérez, Iván +5 more
openaire +2 more sources
AIAccidents due to defective railway lines and derailments are common disasters that are observed frequently in Southeast Asian countries. It is imperative to run proper diagnosis over the detection of such faults to prevent such accidents. However, manual
Salman Ibne Eunus +7 more
doaj +1 more source
Head &Neck, EarlyView.ABSTRACT Background Accurate pretreatment assessment of the extent of tumor invasion and status of cervical lymph node metastasis is essential for staging and treatment planning in HNSCC. Deep learning (DL) shows promise but is limited by methodological heterogeneity. Methods We conducted a systematic review and network meta‐analysis (PRISMA). Studies (
Jannik Ketschau +10 more
wiley +1 more source
Road State Classification of Bangladesh with Convolutional Neural Network Approach [PDF]
Journal of Systemics, Cybernetics and Informatics, 2019 The Traffic congestion is one of the most intricate and challenging problems in all major cities and urban area of Bangladesh. Inadequate road infrastructure is one of the major causes involved with this agonizing issue.
Sajid Ahmed +4 more
doaj
Xception: Deep Learning with Depthwise Separable Convolutions [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution).
openaire +2 more sources
Deep Learning for Endoscopic Classification of Adenoid Hypertrophy
World Journal of Otorhinolaryngology - Head and Neck Surgery, EarlyView.ABSTRACT Objective Endoscopy is a convenient and widely used method to evaluate adenoid size, but the subjectivity of its image diagnosis can result in over‐ or underestimation. To create a new assessment strategy for adenoid hypertrophy, we developed a reliable method for automated classification using a deep learning algorithm with nasal endoscopic ...
Xuan‐Sheng Wang +9 more
wiley +1 more source
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Brain tumour segmentation employing MRI images is important for disease diagnosis, monitoring, and treatment planning. Till now, many encoder‐decoder architectures have been developed for this purpose, with U‐Net being the most extensively utilised. However, these architectures require a lot of parameters to train and have a semantic gap. Some
Muhammad Zeeshan Aslam +3 more
wiley +1 more source
CAAI Transactions on Intelligence Technology, EarlyView.Abstract Abnormalities in the heart's rhythm, known as arrhythmias, pose a significant threat to global health, often leading to severe cardiac conditions and sudden cardiac deaths. Therefore, early and accurate detection of arrhythmias is crucial for timely intervention and potentially life‐saving treatment.
Hasnain Ali Shah +4 more
wiley +1 more source
clcNet: Improving the Efficiency of Convolutional Neural Network using Channel Local Convolutions
, 2018 Depthwise convolution and grouped convolution has been successfully applied to improve the efficiency of convolutional neural network (CNN). We suggest that these models can be considered as special cases of a generalized convolution operation, named ...
Zhang, Dong-Qing
core +1 more source