Deep learning systems for automatic diagnosis of infant cry signals

[1]  Meriem Belguidoum,et al.  EA-based hyperparameter optimization of hybrid deep learning models for effective drug-target interactions prediction , 2021, Expert Syst. Appl..

[2]  Pierre Baldi,et al.  Detecting pulmonary Coccidioidomycosis with deep convolutional neural networks , 2021 .

[3]  Xiran Jiang,et al.  A classification system of day 3 human embryos using deep learning , 2021, Biomed. Signal Process. Control..

[4]  Yuanfan Zhang,et al.  Deep dual-side learning ensemble model for Parkinson speech recognition , 2021, Biomed. Signal Process. Control..

[5]  Michal Byra,et al.  Breast mass classification with transfer learning based on scaling of deep representations , 2021, Biomed. Signal Process. Control..

[6]  SeyyedMohammad JavadiMoghaddam,et al.  A novel deep learning based method for COVID-19 detection from CT image , 2021, Biomedical Signal Processing and Control.

[7]  Houjin Chen,et al.  Tumor detection using deep learning method in automated breast ultrasound , 2021, Biomed. Signal Process. Control..

[8]  Yuhang Xu,et al.  Development and validation of a deep learning-based automatic auscultatory blood pressure measurement method , 2021, Biomed. Signal Process. Control..

[9]  Md. Sipon Miah,et al.  An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models , 2021 .

[10]  Mingyang Li,et al.  FFT-based deep feature learning method for EEG classification , 2021, Biomed. Signal Process. Control..

[11]  Mehmet Feyzi Aksahin,et al.  Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region , 2021, Biomed. Signal Process. Control..

[12]  Chakib Tadj,et al.  Biomedical Diagnosis of Infant Cry Signal Based on Analysis of Cepstrum by Deep Feedforward Artificial Neural Networks , 2021, IEEE Instrumentation & Measurement Magazine.

[13]  Siping Chen,et al.  Can deep learning improve the automatic segmentation of deep foveal avascular zone in optical coherence tomography angiography? , 2021, Biomed. Signal Process. Control..

[14]  Rahul Jain,et al.  COVID-19: Automatic detection from X-ray images by utilizing deep learning methods , 2021, Expert Systems with Applications.

[15]  C. Tadj,et al.  Characterization of infant healthy and pathological cry signals in cepstrum domain based on approximate entropy and correlation dimension , 2021 .

[16]  J. Xie,et al.  Identification of autism spectrum disorder based on short-term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network , 2020, Clinical Neurophysiology.

[17]  Kalyan Chatterjee,et al.  Detection of brain abnormality by a novel Lu-Net deep neural CNN model from MR images , 2020 .

[18]  D. M. Anisuzzaman,et al.  A Deep Learning Study on Osteosarcoma Detection from Histological Images , 2020, Biomed. Signal Process. Control..

[19]  Mohamed Chetouani,et al.  AudVowelConsNet: A Phoneme-Level Based Deep CNN Architecture for Clinical Depression Diagnosis , 2020, Machine Learning with Applications.

[20]  Salim Lahmiri,et al.  Hybrid deep learning convolutional neural networks and optimal nonlinear support vector machine to detect presence of hemorrhage in retina , 2020, Biomed. Signal Process. Control..

[21]  Franz Pernkopf,et al.  Multi-channel lung sound classification with convolutional recurrent neural networks , 2020, Comput. Biol. Medicine.

[22]  Chakib Tadj,et al.  On the use of long-term features in a newborn cry diagnostic system , 2020, Biomed. Signal Process. Control..

[23]  Vered Aharonson,et al.  Assessing Parkinson's disease severity using speech analysis in non-native speakers , 2020, Comput. Speech Lang..

[24]  Manal Abdel Wahed,et al.  Classification of heart sounds using fractional fourier transform based mel-frequency spectral coefficients and traditional classifiers , 2020, Biomed. Signal Process. Control..

[25]  P. Baldi,et al.  Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data. , 2020, Transplantation proceedings.

[26]  Earl K. Miller,et al.  Sensory processing and categorization in cortical and deep neural networks , 2019, NeuroImage.

[27]  Chakib Tadj,et al.  Identification of Diseases in Newborns Using Advanced Acoustic Features of Cry Signals , 2019, Biomed. Signal Process. Control..

[28]  Haydar Ankishan,et al.  Estimation of heartbeat rate from speech recording with hybrid feature vector (HFV) , 2019, Biomed. Signal Process. Control..

[29]  Zhang Yi,et al.  Automated diagnosis of breast ultrasonography images using deep neural networks , 2019, Medical Image Anal..

[30]  Hye Jin Kam,et al.  Learning representations for the early detection of sepsis with deep neural networks , 2017, Comput. Biol. Medicine.

[31]  Chakib Tadj,et al.  Automatic detection of the expiratory and inspiratory phases in newborn cry signals , 2015, Biomed. Signal Process. Control..

[32]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[33]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[34]  D.P. Skinner,et al.  The cepstrum: A guide to processing , 1977, Proceedings of the IEEE.

[35]  Xiao Zheng,et al.  An Attention-based Bi-LSTM Method for Visual Object Classification via EEG , 2021, Biomed. Signal Process. Control..

[36]  Can Eyupoglu,et al.  An epileptic seizure detection system based on cepstral analysis and generalized regression neural network , 2018 .

[37]  Manfredo Atzori,et al.  Deep learning with convolutional neural networks: a resource for the control of robotic prosthetic hands via electromyography , 2016 .

[38]  Venkatanareshbabu Kuppili,et al.  Clinical Epidemiology and Global Health , 2019 .