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Assist-Dermo: A Lightweight Separable Vision Transformer Model for Multiclass Skin Lesion Classification.

Qaisar Abbas1, Yassine Daadaa1, Umer Rashid2

  • 1College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.

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|August 12, 2023
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Summary
This summary is machine-generated.

This study introduces Assist-Dermo, an efficient deep learning system for classifying pigmented skin lesions (PSLs). It achieves high accuracy in detecting skin cancer early, outperforming existing methods with fewer parameters.

Keywords:
SqueezeNetclassificationdeep learningdepthwise separable CNNdermoscopypigmented skin lesionsskin cancervision transformers

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Area of Science:

  • Dermatology and Artificial Intelligence
  • Medical Image Analysis
  • Computer Vision for Healthcare

Background:

  • Automated classification of pigmented skin lesions (PSLs) aids early skin cancer detection.
  • Existing systems often require high computational resources, limiting deployment on constrained devices.
  • There is a need for accurate, yet computationally efficient, deep learning models for PSL classification.

Purpose of the Study:

  • To develop an automatic classification system, Assist-Dermo, for recognizing nine classes of PSLs.
  • To design a separable vision transformer (SVT) architecture with fewer parameters and comparable accuracy to state-of-the-art (SOTA) models.
  • To improve runtime performance and diagnostic efficacy for clinical experts.

Main Methods:

  • Developed a novel SVT architecture integrating SqueezeNet and depthwise separable convolutional neural network (CNN) models.
  • Employed data augmentation to address PSL imbalance and pre-processing for lesion region selection and enhancement.
  • Utilized a diverse dataset including Ph2, ISBI-2017, HAM10000, and ISIC for training and evaluation.

Main Results:

  • Achieved high performance metrics: 95.6% accuracy (ACC), 96.7% sensitivity (SE), 95% specificity (SP), and 0.95 area under the curve (AUC).
  • The Assist-Dermo system demonstrated superior performance compared to SOTA algorithms in classifying nine PSL classes.
  • The model's efficiency was validated through its reduced parameter count and improved runtime performance.

Conclusions:

  • The Assist-Dermo system offers a computationally efficient and accurate solution for classifying pigmented skin lesions.
  • It effectively assists dermatologists in early skin cancer detection through dermoscopy.
  • The developed model code is publicly available on GitHub, promoting further research and development.