Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models - Chapter 6 - dataset -Deep Learning Models Evolution Applied to Biomedical Engineering

Published: 6 June 2022| Version 1 | DOI: 10.17632/9k49p7t7zx.1
Jorge Garza-Ulloa


In this chapter we focus on studying “Deep Learning Models Evolution” that combine midlevel elements with different connections “ANN” types to form more complex network types such as “Recurrent Neural Networks,” “Memory Augmented Neural Networks,” “Modular Neural Networks,” and “Evolutive Neural Networks". Please read this chapter at Science Direct: or buy the book/eBook at Elsevier, Amazon, and Bookstore worldwide. Section Research 6.1 LSTM to classify videos about human body movements and detect human falls (folder MATLAB_LTSM_videos) Section Research 6.2 Regional-CNN model for object detection of breast tumor in mammogram (folder MATLAB_R-CNN) Section Research 6.3 Hopfield Network model to reconstruct noisy chest X-ray images (folder MATLAB_HN) Section Research 6.4 Restricted Boltzmann Machine model to reconstruct noisy chest X-ray images (folder MATLAB_RBM) Section Research 6.5 Create a Reservoir Computing approach for a simulation of “Liquid State Machine (LSM)” of node neurons from “Spiking Neural Networks (SNN)” based on the “Izhikevich neuronal mathematical model” to differentiate normal and pneumonia on chest X-rays (folder MATLAB_LSM) Section Research 6.6 simulation of a Turing Machine (TM) using a recursive function to "analyze how the respiratory transmission of a COVID-19 infected person can spread a virus through droplets/mini-droplets emissions" (folder MATLAB_NTM) Section Research 6.7 Create a Deep Belief Network model to analyze and differentiate normal and pneumonia chest X-rays. “DBN model” from MATLAB based on node neurons from “Spiking Neural Networks (SNN)” applying the “Siegert Neurons mathematical model” to “differentiate normal and pneumonia chest X-rays” (folder MATLAB_DBN) Note*: Please see an “Attention Networks” example at Chapter 7, Research 7.2 “Attention network using Long/Short-Term Memory to Classify Text of COVID-19 symptoms”.



University of Texas at El Paso


Evolutionary Computation, Deep Learning