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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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Classification of Epithelial Tissues: Overview01:22

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Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Updated: Jul 13, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review.

Taye Girma Debelee1,2

  • 1Ethiopian Artificial Intelligence Institute, Addis Ababa 40782, Ethiopia.

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|October 14, 2023
PubMed
Summary
This summary is machine-generated.

This survey reviews recent machine learning and computer vision methods for skin lesion analysis, covering classification, segmentation, and detection. It highlights advancements, challenges, and future directions in dermatological research for early disease detection.

Keywords:
cancerclassificationdeep learningdetectionmachine learningmelanomasegmentationskinskin cancerskin disease

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Area of Science:

  • Dermatology and Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Skin lesion analysis is crucial for diagnosing dermatological disorders.
  • Traditional methods face challenges in accuracy and efficiency.
  • Advancements in computer vision and machine learning offer new possibilities.

Purpose of the Study:

  • To provide a comprehensive review of recent learning-based methods for skin lesion analysis.
  • To examine techniques for skin lesion classification, segmentation, and detection.
  • To identify current trends, challenges, and future research directions.

Main Methods:

  • Systematic review of state-of-the-art research papers.
  • Analysis of deep learning and conventional machine learning techniques.
  • Examination of segmentation algorithms (deep-learning-based, graph-based, region-based).

Main Results:

  • Detailed review of skin lesion classification methods using various image formats.
  • Exploration of segmentation and detection techniques for precise lesion border identification.
  • Discussion of key datasets, challenges, and evaluation metrics in the field.

Conclusions:

  • Machine learning significantly enhances skin lesion analysis capabilities.
  • Accurate classification, segmentation, and detection are vital for improved patient outcomes.
  • Further research is needed to address existing challenges and explore future directions.