Deep CNN Depth Decision in Intra Prediction

2021 ◽  
pp. 1-10
Author(s):  
Helen K. Joy ◽  
Manjunath R. Kounte
Keyword(s):  
Intra Prediction ◽  
Deep Cnn

Adaptive Intra Prediction Mode Signaling Approach in H.264/AVC

2014 ◽  
Vol E97.B (9) ◽  
pp. 1990-1999
Author(s):  
Kibaek KIM ◽  
Gwanggil JEON ◽  
Jechang JEONG
Keyword(s):  
Intra Prediction ◽  
Prediction Mode

Research and Implementation of Fast Algorithm for Intra Prediction Mode Selection Oriented to Hardware

2019 ◽  
Vol 31 (1) ◽  
pp. 158
Author(s):  
Xiuzhi Yang ◽  
Min Zhao ◽  
Longzhao Shi ◽  
Zhifeng Chen ◽  
Mingkui Zheng
Keyword(s):  
Fast Algorithm ◽  
Mode Selection ◽  
Intra Prediction ◽  
Prediction Mode

EczemaNet: A Deep CNN-based Eczema Diseases Classification

2020 ◽  
Author(s):  
Masum Shah Junayed ◽  
Abu Noman Md Sakib ◽  
Nipa Anjum ◽  
Md Baharul Islam ◽  
Afsana Ahsan Jeny
Keyword(s):  
Deep Cnn

scholarly journals Combined deep CNN–LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Da Un Jeong ◽  
Ki Moo Lim
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Measurement ◽  
Patient Specific ◽  
Peak Time ◽  
Simultaneous Prediction ◽  
Deep Cnn ◽  
Pressure Estimation ◽  
The Difference ◽  
Blood Pressure Estimation ◽  
Continuous Blood Pressure

AbstractThe pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (PPG) signal, is an indicator that enables noninvasive and continuous blood pressure estimation. However, it is difficult to accurately measure PAT from ECG and PPG signals because they have inconsistent shapes owing to patient-specific physical characteristics, pathological conditions, and movements. Accordingly, complex preprocessing is required to estimate blood pressure based on PAT. In this paper, as an alternative solution, we propose a noninvasive continuous algorithm using the difference between ECG and PPG as a new feature that can include PAT information. The proposed algorithm is a deep CNN–LSTM-based multitasking machine learning model that outputs simultaneous prediction results of systolic (SBP) and diastolic blood pressures (DBP). We used a total of 48 patients on the PhysioNet website by splitting them into 38 patients for training and 10 patients for testing. The prediction accuracies of SBP and DBP were 0.0 ± 1.6 mmHg and 0.2 ± 1.3 mmHg, respectively. Even though the proposed model was assessed with only 10 patients, this result was satisfied with three guidelines, which are the BHS, AAMI, and IEEE standards for blood pressure measurement devices.

scholarly journals Multiclass Wound Image Classification using an Ensemble Deep CNN-based Classifier

2021 ◽  
pp. 104536
Author(s):  
Behrouz Rostami ◽  
D.M. Anisuzzaman ◽  
Chuanbo Wang ◽  
Sandeep Gopalakrishnan ◽  
Jeffrey Niezgoda ◽  
...  
Keyword(s):  
Image Classification ◽  
Deep Cnn

scholarly journals Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 33438-33463
Author(s):  
Abhijith Reddy Beeravolu ◽  
Sami Azam ◽  
Mirjam Jonkman ◽  
Bharanidharan Shanmugam ◽  
Krishnan Kannoorpatti ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Cnn
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