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Related Experiment Video

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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A novel artificial intelligence-based predictive analytics technique to detect skin cancer.

Prasanalakshmi Balaji1, Bui Thanh Hung1, Prasun Chakrabarti2

  • 1Data Science Laboratory, Faculty of Information Technology, Industrial University of Ho Chi Minh City, Vietnam.

Peerj. Computer Science
|June 22, 2023
PubMed
Summary

Early detection of skin cancer, including melanoma, is crucial for treatment. This study introduces an Artificial Golden Eagle-based Random Forest (AGEbRF) model for accurate skin cancer segmentation from dermoscopic images.

Keywords:
Artificial intelligenceDeep learningMachine learningMalignant tumorsSkin cancer

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Skin cancer is a leading global cause of death, with early detection critical for melanoma curability.
  • Over 75% of worldwide fatalities are linked to skin cancer, highlighting the need for improved diagnostic tools.

Purpose of the Study:

  • To develop and evaluate a novel Artificial Golden Eagle-based Random Forest (AGEbRF) model for early skin cancer cell prediction.
  • To accurately identify and segment cancerous areas in dermoscopic images.

Main Methods:

  • Utilized dermoscopic images as the dataset for training the AGEbRF model.
  • Employed the AGEbRF algorithm for processing image data to detect and segment skin cancer.
  • Simulated the approach using a Python program and compared performance against existing studies.

Main Results:

  • The proposed AGEbRF model demonstrated superior accuracy in predicting skin cancer through image segmentation compared to other models.
  • The model effectively identified and segmented affected areas in dermoscopic images.

Conclusions:

  • The novel AGEbRF model shows significant potential for improving early skin cancer detection and segmentation.
  • This AI-driven approach offers a promising advancement in dermatological diagnostics for better patient outcomes.