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Smart IoT with the hybrid evolutionary method and image processing for tumor detection.

Yan Gao1

  • 1School of Electrical and Mechanical Engineering, Xuchang University, Xuchang, 461000, Henan, China. sherley1982@sina.com.

Scientific Reports
|August 24, 2025
PubMed
Summary

This study introduces a fog computing framework for efficient brain tumor detection, optimizing data placement with an enhanced evolutionary technique (HETS-IP) for reduced energy and faster processing. The system achieves 97% accuracy in classifying tumors.

Keywords:
Data placementFog computingImage processingIoTTumor detection

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Area of Science:

  • Medical Imaging and Diagnostics
  • Computer Science and Engineering
  • Healthcare Informatics

Background:

  • Modern healthcare systems aim for public health enhancement through efficient, reliable solutions, driving advancements and complexity.
  • Expansion of healthcare services introduces challenges like high data volume, latency, and security vulnerabilities.
  • Fog computing offers a solution by processing data closer to end devices, reducing latency and enabling real-time responses.

Purpose of the Study:

  • To propose a robust brain tumor detection framework within a fog-based smart healthcare infrastructure.
  • To optimize fog node placement using an improved evolutionary technique for Image Processing (HETS-IP) based on energy efficiency and latency.
  • To enhance real-time processing of tumor detection by performing the framework directly at fog nodes.

Main Methods:

  • Data placement optimization using HETS-IP, an enhanced Particle Swarm Optimization (PSO) with direct binary encoding.
  • Brain tumor detection framework involving preprocessing (bilateral filter), feature extraction (statistical texture features), and classification (deep Convolutional Neural Network - CNN).
  • Comparative analysis of HETS-IP against traditional evolutionary algorithms (ACO, GASA, GA).

Main Results:

  • HETS-IP demonstrated superior performance over ACO, GASA, and GA in simulations.
  • HETS-IP achieved average reductions in energy consumption (5-14%) and makespan (4-11%) compared to other algorithms.
  • The proposed approach attained a high accuracy of 97% and precision of 96% for brain tumor detection.

Conclusions:

  • The developed fog-based framework effectively addresses challenges in smart healthcare for brain tumor detection.
  • HETS-IP provides significant improvements in energy efficiency and processing time for fog node placement.
  • The framework ensures highly reliable and accurate brain tumor classification, crucial for timely medical intervention.