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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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Related Experiment Video

Updated: Nov 22, 2025

Detection and Isolation of Circulating Melanoma Cells using Photoacoustic Flowmetry
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Melanoma Detection Using Spatial and Spectral Analysis on Superpixel Graphs.

Mahmoud H Annaby1, Asmaa M Elwer2, Muhammad A Rushdi3

  • 1Department of Mathematics, Faculty of Science, Cairo University, Giza, Egypt.

Journal of Digital Imaging
|January 8, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel melanoma detection method using superpixel graphs and dermoscopic image features. The approach significantly enhances early skin cancer diagnosis accuracy.

Keywords:
DermoscopyGraph Fourier transformGraph theoryMachine learningMelanomaSuperpixels

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Computer Science

Background:

  • Early detection of melanoma, the deadliest skin cancer, is crucial for patient survival.
  • Current image processing methods for melanoma detection rely heavily on image representations and feature extraction.
  • Existing techniques show variable performance based on the chosen features.

Purpose of the Study:

  • To develop an improved melanoma detection approach by integrating graph-theoretic representations with conventional dermoscopic image features.
  • To enhance the accuracy and reliability of early melanoma diagnosis from skin lesion images.

Main Methods:

  • Generated superpixels from dermoscopic images to serve as nodes in a superpixel graph.
  • Defined graph edges based on the distance between feature descriptors of adjacent superpixels.
  • Extracted features from both vertex and spectral domains of the graph models, alongside traditional color, geometry, and texture features.
  • Trained and tested conventional and ensemble classifiers on two ISIC archive datasets.

Main Results:

  • The proposed system achieved high performance metrics, including an AUC of [Formula: see text], accuracy of [Formula: see text], specificity of [Formula: see text], and sensitivity of [Formula: see text].
  • Combining graph-theoretic features with conventional features significantly improved detection performance.
  • The superpixel graph representation proved effective for capturing complex lesion characteristics.

Conclusions:

  • The integration of superpixel graph-theoretic representations with conventional features offers a promising approach for accurate melanoma detection.
  • This method has the potential to improve early diagnosis rates and patient outcomes for melanoma.
  • Further research can explore advanced graph signal processing techniques for even greater diagnostic precision.