Application of computer simulation results and machine learning in analysis of microwave radiothermometry data

• URL: http://arxiv.org/abs/2012.10343v1
• Date: Fri, 18 Dec 2020 16:40:36 GMT
• Title: Application of computer simulation results and machine learning in analysis of microwave radiothermometry data
• Authors: Maxim Polyakov, Illarion Popov, Alexander Losev, Alexander Khoperskov
• Abstract summary: The article deals with the machine learning application in the microwave radiothermometry data analysis. With the help of a computer experiment, based on the machine learning algorithms set, the mammary glands temperature fields computer models set adequacy.
• Score: 117.44028458220427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work was done with the aim of developing the fundamental breast cancer early differential diagnosis foundations based on modeling the space-time temperature distribution using the microwave radiothermometry method and obtained data intelligent analysis. The article deals with the machine learning application in the microwave radiothermometry data analysis. The problems associated with the construction mammary glands temperature fields computer models for patients with various diagnostics classes, are also discussed. With the help of a computer experiment, based on the machine learning algorithms set (logistic regression, naive Bayesian classifier, support vector machine, decision tree, gradient boosting, K-nearest neighbors, etc.) usage, the mammary glands temperature fields computer models set adequacy.

Related papers

• Using simulation to calibrate real data acquisition in veterinary medicine [0.1420200946324199]
  This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine. The study harnesses the power of Blender and the Blenderproc library to generate synthetic datasets that reflect diverse anatomical, environmental, and behavioral conditions. The generated data is utilized to train machine learning algorithms for identifying normal and abnormal gaits.
  arXiv  Detail & Related papers  (2023-07-21T16:50:10Z)
• Advancing Reacting Flow Simulations with Data-Driven Models [50.9598607067535]
  Key to effective use of machine learning tools in multi-physics problems is to couple them to physical and computer models. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems.
  arXiv  Detail & Related papers  (2022-09-05T16:48:34Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Approximate Bayesian Computation Based on Maxima Weighted Isolation Kernel Mapping [0.0]
  The work tries to solve the problem of a precise evaluation of a parameter for this type of model. The application of the branching processes model to cancer cell evolution has many difficulties like high dimensionality and the rare appearance of a result of interest.
  arXiv  Detail & Related papers  (2022-01-30T07:11:57Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• Domain Shift in Computer Vision models for MRI data analysis: An Overview [64.69150970967524]
  Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yet only a few applications are now in clinical use. Poor transferability of themodels to data from different sources or acquisition domains is one of the reasons for that.
  arXiv  Detail & Related papers  (2020-10-14T16:34:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.