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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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The skin is divided into epidermis, dermis, and hypodermis, the skin's outermost, middle, and inner layers. The human epidermal layer regularly undergoes renewal, where old, dead cells are replaced by new cells. Epidermal stem cells or EpiSCs divide and differentiate to restore the lost cells. For the renewal process, some EpiSCs continuously self-renew. In contrast, few others differentiate into transit-amplifying cells, which later form prickle or spinous cells, followed by granular...
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Related Experiment Video

Updated: May 28, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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A robust deep learning framework for multiclass skin cancer classification.

Burhanettin Ozdemir1, Ishak Pacal2,3

  • 1Department of Operations and Project Management, College of Business, Alfaisal University, Riyadh, 11533, Saudi Arabia. bozdemir@alfaisal.edu.

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|February 10, 2025
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Summary

This study introduces a hybrid deep learning model for accurate skin cancer diagnosis, outperforming existing methods in classifying skin lesions. The model enhances early detection and treatment efficacy for improved patient survival rates.

Keywords:
ConvNeXtv2HealthMedical image analysisSkin cancer detectionVision Transformer

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Skin cancer is a major global health issue, with early diagnosis crucial for effective treatment and survival.
  • Accurate classification of skin lesions is challenging due to visual similarities between benign and malignant types.

Purpose of the Study:

  • To develop an innovative hybrid deep learning model for enhanced skin lesion classification.
  • To improve the accuracy and efficiency of early skin cancer diagnosis.

Main Methods:

  • A hybrid deep learning model combining ConvNeXtV2 blocks and separable self-attention mechanisms was proposed.
  • The model was trained and validated on the ISIC 2019 dataset, utilizing data augmentation and transfer learning.

Main Results:

  • The proposed model achieved 93.48% accuracy, 93.24% precision, 90.70% recall, and 91.82% F1-score.
  • It outperformed numerous Convolutional Neural Network (CNN) and Vision Transformer (ViT) based models.
  • The model has a compact design with 21.92 million parameters, ensuring efficiency.

Conclusions:

  • The developed model demonstrates high accuracy and generalizability for diverse skin lesion classification.
  • It offers a reliable framework for early and precise skin cancer diagnosis in clinical settings.