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Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things.

Alhassan Mabrouk1, Abdelghani Dahou2, Mohamed Abd Elaziz3,4,5

  • 1Mathematics and Computer Science Department, Faculty of Science, Beni-Suef University, Beni Suef 62511, Egypt.

Computational Intelligence and Neuroscience
|July 25, 2022
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Summary
This summary is machine-generated.

This study introduces an Internet of Medical Things (IoMT) approach for classifying medical images using transfer learning and chaos game optimization. The method enhances disease detection accuracy, potentially leading to earlier treatment and reduced mortality rates.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Internet of Medical Things (IoMT)

Background:

  • The Internet of Medical Things (IoMT) offers widespread access to medical professionals but faces challenges in efficient automatic disease detection.
  • Existing IoMT approaches for conditions like melanoma and leukemia lack high diagnostic efficiency, delaying patient treatment.

Purpose of the Study:

  • To propose an ubiquitous Internet of Medical Things (IoMT) approach for enhanced medical image classification.
  • To improve disease detection accuracy and efficiency in IoMT applications for earlier patient intervention.

Main Methods:

  • A two-stage methodology was developed for medical image classification within an IoMT framework.
  • Stage 1: Feature extraction using transfer learning (TL) with MobileNetV3.
  • Stage 2: Feature selection via chaos game optimization (CGO) to refine performance.

Main Results:

  • The proposed IoMT approach achieved high accuracy across multiple datasets: 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell datasets.
  • The methodology demonstrated superior performance metrics compared to existing methods in medical image classification.
  • The CGO-based feature selection effectively reduced unnecessary features, enhancing overall model efficiency.

Conclusions:

  • The developed IoMT approach significantly improves medical image classification accuracy and efficiency.
  • This ubiquitous method holds potential for earlier disease diagnosis and improved patient outcomes.
  • The integration of transfer learning and chaos game optimization offers a promising direction for IoMT-based healthcare solutions.