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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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Melanoma diagnosis using deep learning techniques on dermatoscopic images.

Mario Fernando Jojoa Acosta1, Liesle Yail Caballero Tovar2, Maria Begonya Garcia-Zapirain1

  • 1eVida Research Laboratory, University of Deusto, Avda. Universidades 24, 48007, Bilbao, Spain.

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Early melanoma detection is crucial for survival. This study introduces a deep learning model that accurately classifies skin lesions as benign or malignant from dermatoscopic images, improving diagnostic accuracy.

Keywords:
Convolutional neural networkDeep learningMask R_CNNObject classificationObject detectionTransfer learning

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Melanoma incidence has risen significantly over the last 30 years.
  • Early detection of melanoma is critical for reducing mortality rates.
  • Automated systems for melanoma detection in dermatoscopic images can aid medical diagnosis.

Purpose of the Study:

  • To develop and evaluate a reliable, automated system for detecting melanoma using deep learning.
  • To improve the accuracy and reliability of classifying skin lesions as benign or malignant.

Main Methods:

  • Utilized a two-stage deep learning approach.
  • Employed Mask and Region-based Convolutional Neural Networks for region of interest cropping.
  • Implemented a ResNet152 structure for lesion classification (benign/malignant).

Main Results:

  • The proposed model demonstrated increased accuracy (3.66%) and balanced accuracy (9.96%) compared to existing methods.
  • Achieved simultaneous high scores for specificity and sensitivity (greater than 0.8).
  • Indicated effective discrimination between benign and malignant lesions without class bias.

Conclusions:

  • The developed model shows significant performance improvement in classifying skin lesions.
  • The deep learning approach offers a promising tool for accurate melanoma detection.
  • The model's ability to simultaneously achieve high sensitivity and specificity enhances its clinical utility.