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The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study

The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study

Res Net was created specially to deal with this problem [27,28]. The Res Net50 architecture is based on the Res Net34 paradigm, except that every component is composed of a series of 3 layers instead of 2. This model is far better than the 34-layer version of Res Net and produces 3.8 billion floating-point operations per second. Each of the preceding 2-layer blocks was swapped out for a 3-layer bottleneck block to produce a 50-layer design [29].

Shishir Shetty, Auwalu Saleh Mubarak, Leena R David, Mhd Omar Al Jouhari, Wael Talaat, Natheer Al-Rawi, Sausan AlKawas, Sunaina Shetty, Dilber Uzun Ozsahin

JMIR Form Res 2024;8:e57335

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Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study

A total of 7 classical time series deep neural networks as follows were pre-evaluated on a portion of PSD and raw EEG data of the training data set (n=100, randomly selected participants from the training data set): Fully Convolutional Neural Network (FCN), Residual Network (t-Res Net), Encoder, Multi-Scale Convolutional Neural Network, Time Le-Net (t-Le NET), Multi-Channel Deep Convolutional Neural Network, and Time Convolutional Neural Network.

Shahab Haghayegh, Kun Hu, Katie Stone, Susan Redline, Eva Schernhammer

J Med Internet Res 2023;25:e40211

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State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review

In addition to CNN and Res Net architectures, recurrent neural networks (RNNs) represent another type of DL technique frequently used in health care. Disease prediction [24], biomedical image segmentation [25], and obstructive sleep apnea detection [26] are only a few of their applications.

Georgios Petmezas, Leandros Stefanopoulos, Vassilis Kilintzis, Andreas Tzavelis, John A Rogers, Aggelos K Katsaggelos, Nicos Maglaveras

JMIR Med Inform 2022;10(8):e38454

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