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Skin lesion classification using HG-PSO and YOLOv7 based convolutional network in real time.

Hera Shaheen1, Maheshwari Prasad Singh1

  • 1Department of Computer Science and Engineering, National Institute of Technology Patna, Patna, Bihar, India.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
|October 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hybrid optimization and deep learning method for accurate skin cancer classification from dermoscopic images. The approach enhances early detection and diagnosis, improving patient outcomes in clinical settings.

Keywords:
Deep learningHAM10000 datasetdermoscopic imagesskin cancerskin lesions classification

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Skin cancer diagnosis relies on visual inspection and dermoscopy, demanding precise lesion localization and classification.
  • Early and accurate diagnosis is critical for effective treatment and improved patient prognosis.
  • Existing methods face challenges in achieving high accuracy and efficiency in classifying skin lesions.

Purpose of the Study:

  • To develop and evaluate a hybrid optimization and deep learning model for enhanced skin cancer classification.
  • To improve the accuracy, precision, and recall in identifying and classifying skin lesions from dermoscopic images.
  • To assess the method's performance against state-of-the-art techniques on diverse datasets.

Main Methods:

  • A hybrid approach combining Genetic and Particle Swarm Optimization (HG-PSO) with a You Only Look Once version 7 (YOLOv7) convolutional neural network was proposed.
  • Optimized YOLOv7 was used for initial lesion detection, followed by color thresholding for segmentation.
  • The segmented regions were then classified using the proposed convolutional network.

Main Results:

  • The method achieved high performance across multiple datasets: 98.86% accuracy on HAM10000, 97.10% on ISIC-2019, and 97.7% on PH2.
  • Excellent average precision, recall, and F1-scores were recorded, demonstrating robust classification capabilities.
  • The model exhibited fast processing times (2-3 seconds), suitable for real-time applications like telemedicine.

Conclusions:

  • The proposed HG-PSO and YOLOv7-based method offers a significant advancement in automated skin cancer classification.
  • Its high accuracy and efficiency make it a promising tool for early detection and diagnosis of skin cancer.
  • The method's speed and performance suggest potential for integration into clinical workflows and telemedicine platforms.