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
Contributor:
Jorge Garza-Ulloa

Description

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: https://www.sciencedirect.com/science/article/pii/B978012820718500012X or buy the book/eBook at Elsevier, Amazon, and Bookstore worldwide. Section 6.2.3.1 Research 6.1 LSTM to classify videos about human body movements and detect human falls (folder MATLAB_LTSM_videos) Section 6.2.5.1 Research 6.2 Regional-CNN model for object detection of breast tumor in mammogram (folder MATLAB_R-CNN) Section 6.2.6.1 Research 6.3 Hopfield Network model to reconstruct noisy chest X-ray images (folder MATLAB_HN) Section 6.2.8.1 Research 6.4 Restricted Boltzmann Machine model to reconstruct noisy chest X-ray images (folder MATLAB_RBM) Section 6.2.10.1 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 6.3.2.1 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 6.4.1.1 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”.

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Institutions

University of Texas at El Paso

Categories

Evolutionary Computation, Deep Learning

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