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Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms.

Christian Tchito Tchapga1, Thomas Attia Mih1, Aurelle Tchagna Kouanou1,2

  • 1College of Technology, University of Buea, Buea, Cameroon.

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Summary
This summary is machine-generated.

This study surveys machine learning classification algorithms for biomedical images. It proposes a workflow using Support Vector Machines and Deep Learning on a big data architecture for improved disease diagnosis.

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Computer-Aided Diagnosis

Background:

  • Medical imaging generates vast amounts of data for disease identification.
  • Machine learning classification is crucial for categorizing images and aiding diagnosis.
  • Existing methods require efficient processing of large biomedical datasets.

Purpose of the Study:

  • To survey classification algorithms for biomedical image analysis.
  • To explore the application of these algorithms within a big data architecture using Spark.
  • To propose an optimized classification workflow for computer-aided diagnosis.

Main Methods:

  • Literature review of classification algorithms for biomedical images.
  • Integration of machine learning algorithms with the Spark big data framework.
  • Development of a feature extraction algorithm adaptable to the classification workflow.

Main Results:

  • Identification of Support Vector Machine and Deep Learning as optimal classification algorithms.
  • Demonstration of applying these algorithms within a big data architecture.
  • Proposal of a customizable feature extraction method.

Conclusions:

  • Machine learning classification, particularly with Support Vector Machines and Deep Learning, is effective for biomedical image analysis.
  • The proposed workflow integrates these algorithms with big data processing for enhanced computer-aided diagnosis.
  • The feature extraction algorithm offers flexibility for various classification tasks.