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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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The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
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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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Related Experiment Video

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Multiscale Feature Fusion for Skin Lesion Classification.

Gang Wang1,2, Pu Yan1,3, Qingwei Tang1,2

  • 1College of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230000, China.

Biomed Research International
|January 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multiscale feature fusion model for improved skin lesion classification using convolutional neural networks (CNNs). The model enhances early skin cancer detection accuracy, potentially reducing patient mortality.

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Area of Science:

  • Dermatology
  • Computer-Aided Diagnosis
  • Artificial Intelligence

Background:

  • Skin cancer poses a significant mortality risk, underscoring the need for accurate and early detection methods.
  • Convolutional neural networks (CNNs) show promise in computer-aided diagnosis for medical imaging.
  • Existing CNN models may require enhancement for precise skin lesion classification.

Purpose of the Study:

  • To develop an advanced multiscale feature fusion model for enhanced skin lesion classification.
  • To improve the diagnostic accuracy of CNNs in identifying skin cancer.
  • To reduce mortality rates through earlier and more precise detection of skin lesions.

Main Methods:

  • A two-stream network architecture combining DenseNet-121 and an improved VGG-16 was employed.
  • A feature fusion module with multireceptive fields was designed to capture multiscale pathological information.
  • Generalized mean pooling (GeM pooling) was utilized to reduce the spatial dimensionality of lesion features.

Main Results:

  • The proposed model achieved a test accuracy of 91.24% on the ISIC2018 dataset.
  • Macro-average performance reached 95%, indicating robust classification capabilities.
  • The system demonstrated good classification performance for skin lesions.

Conclusions:

  • The multiscale feature fusion model significantly improves skin lesion classification accuracy.
  • This approach offers a promising tool for computer-aided diagnosis in dermatology.
  • Enhanced classification accuracy can contribute to earlier detection and reduced skin cancer mortality.