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Related Experiment Videos

Multi skin lesions classification using fine-tuning and data-augmentation applying NASNet.

Elia Cano1, José Mendoza-Avilés1, Mariana Areiza1

  • 1Computer Science, Universidad Tecnológica de Panamá, Panama, Panama.

Peerj. Computer Science
|June 21, 2021
PubMed
Summary

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This study introduces a novel Convolutional Neural Network (CNN) approach using NASNet architecture for accurate skin disease classification from images. The method enhances early detection and prevention of skin conditions.

Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Skin lesions are common indicators of various diseases, including skin cancer.
  • Climate change and treatment costs necessitate improved skin cancer prevention strategies.
  • Computational methods, particularly Convolutional Neural Networks (CNNs), are increasingly used for medical image analysis.

Purpose of the Study:

  • To develop an accurate, automated system for classifying eight common skin diseases without segmentation.
  • To leverage the NASNet architecture for enhanced performance in skin lesion recognition.
  • To compare the proposed CNN model's efficacy against other trained architectures.

Main Methods:

  • Utilized a NASNet-based CNN architecture trained end-to-end on augmented skin lesion images from the International Skin Imaging Collaboration (ISIC) dataset.
Keywords:
Convolutional neural networkImage analysisNASNetSkin cancerSkin diseases

Related Experiment Videos

  • Initialized CNN weights from ImageNet and fine-tuned for discriminating between skin lesions.
  • Applied 10-fold cross-validation for robust evaluation and calculated accuracy, sensitivity, and specificity.

    • Main Results:

    • The proposed NASNet-based CNN system achieved higher accuracy compared to other evaluated CNN architectures.
    • Demonstrated a significant reduction in training parameters, suggesting computational efficiency.
    • The model accurately classified eight different types of skin diseases without requiring image segmentation.

    Conclusions:

    • The NASNet architecture offers a novel and effective approach for multi-class skin disease classification using medical images.
    • This system represents a significant advancement in automated skin lesion analysis and early disease detection.
    • The findings highlight the potential of deep learning for improving skin cancer prevention and diagnosis.