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New Geometric Deep Learning Model for Detecting Stroke Lesions

The proposed approach outperforms other neural network architectures by leveraging rich geometric information to segment brain stroke lesion images ....

Convolutional Network , Medical Imaging , Gold Open Access ,

Geometric Deep Learning Model Detects Stroke Lesions

Geometric Deep Learning Model Detects Stroke Lesions
miragenews.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from miragenews.com Daily Mail and Mail on Sunday newspapers.

Convolutional Network , Medical Imaging , Gold Open Access ,

Novel geometric deep learning model improves stroke lesion segmentation

Ischemic stroke, which occurs when a blood vessel in the brain gets blocked by a clot, is among the leading causes of death worldwide. ....

Megan Craig , Convolutional Network , Medical Imaging , Deep Learning , Blood Vessel , Computed Tomography , Imaging Techniques , Ischemic Stroke , Medical Imaging ,

Team develops new geometric deep learning model for detecting stroke lesions

Team develops new geometric deep learning model for detecting stroke lesions
medicalxpress.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from medicalxpress.com Daily Mail and Mail on Sunday newspapers.

Ariel Iporre Rivas , Convolutional Network , Medical Imaging ,

"Convolutional Deep Neural Network and Full Connectivity for Speech Enh" by Ban M. Alameri, Inas Jawad Kadhim et al.

The speech signal that is received in real-time has background noise and reverberations, which have an impact on the quality of speech. Therefore, it is crucial to reduce or eliminate the noise and increase the intel-ligibility and quality of speech signals. In this study, a proposed method that is the most effective and challenging in a low SNR environment for three types of noise are removed, including washing machine, traffic noise, and electric fan noise, and clean speech is recovered. with three samples of noise which are mixed and added to the clean speech signal with a lower level of SNR value fixed at (-5, 0, 5) dBs, that noise source takes equal weights. The enhancement of the corrupted speech signal is done by applying a fully connected and convo-lutional neural network-based denoising algorithm and comparing their perfor-mance. The proposed network shows that a fully connected network (FCN) has less elapsed time than a convolutional network (CNN) while still achieving better ....

Convolutional Network , Deep Learning , Ully Connected Network , Ignal To Noise Ratio Snr , Speech Enhancement ,