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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.
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Related Experiment Video

Updated: Oct 3, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images.

Ranpreet Kaur1, Hamid GholamHosseini1, Roopak Sinha1

  • 1School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a lightweight deep convolutional neural network (DCNN) for automated melanoma detection. The DCNN accurately classifies malignant versus benign melanoma from dermoscopic images, improving diagnostic efficiency.

Keywords:
classificationdeep convolutional neural networksmelanomaskin cancer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Dermatology

Background:

  • Melanoma detection from dermoscopic images is complex.
  • Deep learning offers potential solutions for automated analysis.
  • Existing methods may lack efficiency or simplicity.

Purpose of the Study:

  • To develop a lightweight and efficient deep convolutional neural network (DCNN) for automated melanoma classification.
  • To accurately distinguish between malignant and benign melanoma.
  • To provide an advanced yet less complex framework for melanoma diagnosis.

Main Methods:

  • A novel deep convolutional neural network (DCNN) architecture was designed.
  • The DCNN incorporates multiple layers for feature extraction.
  • The model was trained and validated using dermoscopic images from ISIC datasets (2016, 2017, 2020).

Main Results:

  • The proposed DCNN achieved high classification accuracies: 81.41% (ISIC 2016), 88.23% (ISIC 2017), and 90.42% (ISIC 2020).
  • Performance metrics included accuracy, precision, recall, specificity, and F1-score.
  • The DCNN demonstrated superior performance compared to state-of-the-art networks.

Conclusions:

  • The developed DCNN offers an efficient and less complex approach for automated melanoma detection.
  • This automated system can expedite the diagnostic process.
  • The findings support the potential of AI in improving melanoma identification and patient outcomes.