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Computer-aided diagnosis systems: a comparative study of classical machine learning versus deep learning-based

Ramzi Guetari1, Helmi Ayari1, Houneida Sakly2

  • 1SERCOM Laboratory, Polytechnic School of Tunisia, University of Carthage, PO Box 743, La Marsa, 2078 Tunisia.

Knowledge and Information Systems
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) models offer improved diagnostic accuracy in healthcare, aiding tumor classification and COVID-19 detection. The choice between classical and deep learning approaches depends on dataset size for optimal patient outcomes.

Keywords:
Computer-aided diagnosis system (CAD)Convolutional neural networkDeep learningFeature extractionMachine learningTumor classification

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Diagnostic Systems

Background:

  • Accurate diagnosis is critical for patient outcomes, yet variations in clinical judgment can lead to suboptimal or fatal treatments.
  • Machine learning (ML) offers automated data analysis to create predictive models, optimizing diagnosis and potentially saving lives.

Purpose of the Study:

  • To review and evaluate different machine learning models for tumor classification and COVID-19 infection detection.
  • To compare classical computer-aided diagnosis (CAD) systems with deep learning-based CAD systems.

Main Methods:

  • Review of various machine learning models and algorithms used in medical image analysis for classification tasks.
  • Comparison of classical CAD systems relying on manual or separate ML feature extraction versus deep learning CAD systems with automatic feature identification and extraction.

Main Results:

  • Both classical and deep learning-based CAD systems demonstrate comparable performance in diagnostic tasks.
  • The selection of the appropriate CAD system is dataset-dependent.

Conclusions:

  • Manual feature extraction is preferred for small datasets in medical image analysis.
  • Deep learning approaches are more suitable for larger datasets, offering automated feature extraction for improved diagnostic accuracy.