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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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Classification of Epithelial Tissues: Stratified Epithelium01:29

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Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...
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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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Skin Diseases and Disorders01:23

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Skin is the first line of defense and encounters a variety of microbes. Some pathogenic strains are often the cause of a broad range of infections of the skin and other body systems. These conditions can affect people of all ages and may have different causes, including genetic factors, infections, autoimmune reactions, environmental factors, and lifestyle choices.
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The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
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Classification of Leukocytes01:30

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Deep Ensemble Learning for Multiclass Skin Lesion Classification.

Tsu-Man Chiu1,2, I-Chun Chi3, Yun-Chang Li2

  • 1School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan.

Bioengineering (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

Artificial intelligence (AI) enhances skin lesion diagnosis by analyzing medical images. An AI model achieved 98.5% accuracy in identifying seven types of skin conditions, improving early detection.

Keywords:
CNNSwinViTdermoscopic imagesensemble learningskin lesions

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Skin lesions can stem from various conditions like infections, tumors, or autoimmune diseases.
  • Traditional diagnostic methods (visual inspection, palpation) often lack precision.
  • Artificial intelligence (AI) offers improved diagnostic accuracy by detecting subtle patterns in skin images.

Purpose of the Study:

  • To develop and evaluate a multiclass skin lesion diagnostic model.
  • To focus on an Eastern population dataset (CSMUH) for improved generalizability.
  • To leverage advanced AI techniques for enhanced diagnostic performance.

Main Methods:

  • Utilized the CSMUH dataset, categorized into seven disease classes.
  • Fine-tuned 25 pre-trained models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs).
  • Developed an ensemble model (Swin-ViT-EfficientNetB4) using hard and soft voting, validated with randomized experiments and holdout technique.

Main Results:

  • The ensemble model, Swin-ViT-EfficientNetB4, achieved a test accuracy of 98.5%.
  • Demonstrated superior performance in classifying diverse skin lesions.
  • Indicated high reliability through rigorous testing and validation.

Conclusions:

  • The proposed AI ensemble model shows significant potential for accurate and early skin lesion diagnosis.
  • Highlights the effectiveness of AI in dermatological applications.
  • Suggests a promising tool for clinical settings to aid dermatologists.