Sequencing for Encoding in Neuroevolutionary Synthesis of Neural Network Models for Medical Diagnosis

Today, artificial neural networks are actively used for various medical tasks. Diagnostics is one of these tasks that can be significantly optimized by using models that will be based on neural networks. Neuroevolution methods are used for synthesis more adaptive models. The work with such methods begins with the initialization of a population contains individuals, each of which is a separate neural network. For further work with them, encoding is used, that is, a certain representation of information about the neural network. The correct encoding method can significantly simplify and speed up further work, which will reduce resource consumption. In this paper, authors propose a new approach to information encoding during neuroevolutionary synthesis, which will expand the practical use of classical methods.

[1]  Robert J. Brooker,et al.  Genetics: Analysis and Principles , 1998 .

[2]  P. Courtet,et al.  The Future is Knocking: How Artificial Intelligence Will Fundamentally Change Psychiatry , 2020, Academic Psychiatry.

[3]  Alexander M. Rush,et al.  Weightless: Lossy Weight Encoding For Deep Neural Network Compression , 2018, ICML.

[5]  Robert D. Atkinson,et al.  Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again , 2019 .

[6]  Natalia Kryvinska,et al.  Multiple Linear Regression Based on Coefficients Identification Using Non-iterative SGTM Neural-like Structure , 2019, IWANN.

[7]  G. Raghu,et al.  COVID-19 interstitial pneumonia: monitoring the clinical course in survivors , 2020, The Lancet Respiratory Medicine.

[8]  Andrii Oliinyk,et al.  Development of the modified methods to train a neural network to solve the task on recognition of road users , 2019, Eastern-European Journal of Enterprise Technologies.

[9]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[10]  J. Holland.  Signals and Boundaries: Building Blocks for Complex Adaptive Systems , 2012 .

[11]  B. A. Pierce,et al.  Genetics: A Conceptual Approach , 2002 .

[12]  Dr. Alex A. Freitas.  Data Mining and Knowledge Discovery with Evolutionary Algorithms , 2002, Natural Computing Series.

[13]  Gul Muhammad Khan.  Evolution of Artificial Neural Development - In Search of Learning Genes , 2018, Studies in Computational Intelligence.

[14]  Graham Cormode,et al.  Count-Min Sketch , 2016, Encyclopedia of Algorithms.

[15]  Liron Pantanowitz,et al.  An artificial intelligence algorithm for prostate cancer diagnosis in whole slide images of core needle biopsies: a blinded clinical validation and deployment study. , 2020, The Lancet. Digital health.

[16]  Nature-Inspired Computation in Data Mining and Machine Learning , 2020, Studies in Computational Intelligence.

[17]  Sergey Subbotin,et al.  Modification of the Genetic Method for Neuroevolution Synthesis of Neural Network Models for Medical Diagnosis , 2019, CMIS.

[18]  Alexandre Bergel.  Agile Artificial Intelligence in Pharo: Implementing Neural Networks, Genetic Algorithms, and Neuroevolution , 2020 .

[19]  Sergey Subbotin,et al.  Synthesis of Artificial Neural Networks Using a Modified Genetic Algorithm , 2018, IDDM.

[20]  Sergey Subbotin,et al.  Implementation of Selective Pressure Mechanism to Optimize Memory Consumption in the Synthesis of Neuromodels for Medical Diagnostics , 2019, IDDM.

[21]  Ivan Izonin,et al.  SGD-Based Wiener Polynomial Approximation for Missing Data Recovery in Air Pollution Monitoring Dataset , 2019, IWANN.

[22]  Andrii O. Oliinyk,et al.  Factor analysis of transaction data bases , 2014, Automatic Control and Computer Sciences.

[23]  Andrii Oliinyk,et al.  Using Recurrent Neural Networks for Data-Centric Business , 2020 .

[24]  Lia Morra,et al.  Artificial Intelligence in Medical Imaging: From Theory to Clinical Practice , 2019 .

[25]  John J. Georrge,et al.  Artificial Intelligence in Medical Diagnosis: Methods, Algorithms and Applications , 2020, Learning and Analytics in Intelligent Systems.

[26]  M. Revel,et al.  COVID-19 pneumonia: A review of typical CT findings and differential diagnosis , 2020, Diagnostic and Interventional Imaging.

[27]  Ivan Izonin,et al.  An Approach towards Missing Data Recovery within IoT Smart System , 2019, Procedia Computer Science.

[28]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[29]  Sebastian Risi,et al.  Indirectly Encoding Neural Plasticity as a Pattern of Local Rules , 2010, SAB.

[30]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[31]  Won-Il Choi,et al.  Pneumonia severity index in viral community acquired pneumonia in adults , 2019, PloS one.

[32]  A. Oliinyk,et al.  Method of Artificial Neural Network Synthesis for Using in Integrated CAD , 2019, 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM).

[33]  Xu Yang,et al.  Neural Network Evolving Algorithm Based on the Triplet Codon Encoding Method , 2018, Genes.