Convolutional Neural Networks Learning from Respiratory data

Convolutional neural networks (CNNs) have been successfully applied in a wide variety of fields, from image processing to genomic sequencing. In the context of biomedical data, we focus our attention on respiratory data, complex signals characterized by a high level of information richness and potential indicators of several common medical conditions. In this respect, we address the problem of identifying unhealthy indicators in respiratory sound data through the application of novel CNNs architecture and the extraction of Mel Frequency Cepstral Coefficients (MFCC), in order to unveil unhealthy indicators and provide doctors with a potentially life-saving tool.

[1]  Henggui Zhang,et al.  Cardiac left ventricular volumes prediction method based on atlas location and deep learning , 2016, 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[2]  Masakazu Matsugu,et al.  Subject independent facial expression recognition with robust face detection using a convolutional neural network , 2003, Neural Networks.

[3]  Xiaogang Wang,et al.  Medical image classification with convolutional neural network , 2014, 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV).

[4]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[5]  Christopher Joseph Pal,et al.  Brain tumor segmentation with Deep Neural Networks , 2015, Medical Image Anal..

[6]  Wei Li,et al.  RaptorX-Property: a web server for protein structure property prediction , 2016, Nucleic Acids Res..

[7]  O. Stegle,et al.  DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning , 2016, Genome Biology.

[8]  Eugenio Vocaturo,et al.  On the use of Networks in Biomedicine , 2017, FNC/MobiSPC.

[9]  M. Mohammed Thaha,et al.  Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images , 2019, Journal of Medical Systems.

[10]  Y Ichioka,et al.  Parallel distributed processing model with local space-invariant interconnections and its optical architecture. , 1990, Applied optics.

[11]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[12]  Yasubumi Sakakibara,et al.  Convolutional neural networks for classification of alignments of non-coding RNA sequences , 2018, Bioinform..

[13]  Yanjun Qi,et al.  MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-Based Protein Structure Prediction , 2016, AAAI.

[14]  Shu Liao,et al.  Representation Learning: A Unified Deep Learning Framework for Automatic Prostate MR Segmentation , 2013, MICCAI.

[15]  Ronald M. Summers,et al.  Deep convolutional networks for pancreas segmentation in CT imaging , 2015, Medical Imaging.

[16]  Ioanna Chouvarda,et al.  Α Respiratory Sound Database for the Development of Automated Classification , 2017, BHI 2017.

[17]  Jian Zhou,et al.  Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction , 2014, ICML.

[18]  Yang Li,et al.  Malphite: A convolutional neural network and ensemble learning based protein secondary structure predictor , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[19]  Gustavo Carneiro,et al.  Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance , 2017, Medical Image Anal..

[20]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[21]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[22]  Lukasz Kurgan,et al.  DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields , 2015, International journal of molecular sciences.

[23]  Sajad Shirali-Shahreza,et al.  Effect of MFCC normalization on vector quantization based speaker identification , 2010, The 10th IEEE International Symposium on Signal Processing and Information Technology.