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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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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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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Renewal of Skin Epidermal Stem Cells01:12

Renewal of Skin Epidermal Stem Cells

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The skin is divided into epidermis, dermis, and hypodermis, the skin's outermost, middle, and inner layers. The human epidermal layer regularly undergoes renewal, where old, dead cells are replaced by new cells. Epidermal stem cells or EpiSCs divide and differentiate to restore the lost cells. For the renewal process, some EpiSCs continuously self-renew. In contrast, few others differentiate into transit-amplifying cells, which later form prickle or spinous cells, followed by granular...
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Related Experiment Video

Updated: Sep 2, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Skin Cancer Classification With Deep Learning: A Systematic Review.

Yinhao Wu1, Bin Chen2, An Zeng3

  • 1School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou, China.

Frontiers in Oncology
|August 1, 2022
PubMed
Summary

Deep learning models show promise for skin cancer classification, addressing challenges like imbalanced data. Future directions focus on structured, lightweight, and multimodal approaches for improved accuracy and robustness in skin lesion diagnosis.

Keywords:
convolutional neural networkdeep learninggenerative adversarial networksimage classificationskin cancer

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Area of Science:

  • Dermatology and Artificial Intelligence
  • Medical Image Analysis
  • Computational Pathology

Background:

  • Skin cancer classification is critical for early diagnosis and treatment, but faces challenges with imbalanced and limited datasets.
  • Automatic skin cancer classification is hindered by the need for robust models with cross-domain adaptability.
  • Deep learning methods have emerged as powerful tools for skin cancer classification, yet comprehensive reviews addressing current challenges are scarce.

Purpose of the Study:

  • To provide a comprehensive overview of deep learning-based algorithms for skin cancer classification.
  • To summarize frontier problems in skin cancer classification, including data imbalance, limitation, domain adaptation, robustness, and efficiency.
  • To discuss corresponding solutions and future directions in the field.

Main Methods:

  • Review of dermatological image types and publicly available skin cancer datasets.
  • Analysis of convolutional neural network applications in skin cancer classification.
  • Summary of deep learning strategies addressing data imbalance, limited data, domain adaptation, model robustness, and efficiency.

Main Results:

  • Deep learning, particularly convolutional neural networks, has demonstrated significant success in skin cancer classification.
  • Key challenges identified include data imbalance, data scarcity, domain adaptation, model robustness, and efficiency.
  • Solutions involve structured, lightweight, and multimodal deep learning approaches.

Conclusions:

  • Deep learning offers advanced solutions for automated skin cancer classification, overcoming traditional limitations.
  • The future of skin cancer classification lies in developing structured, lightweight, and multimodal deep learning models.
  • Further research is needed to address remaining challenges and capitalize on the potential of deep learning in dermatology.