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Related Concept Videos

Skin Cancer01:30

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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: 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 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 Epithelial Tissues: Simple Epithelium01:30

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Simple epithelium consists of a single layer of cells that lines body cavities and blood vessels. The shape of the cells in the epithelium reflects the function of the tissue. Cells in simple squamous epithelium appear as thin scales with flat, elliptical nuclei that mirror the form of the cell.
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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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Classification of Epithelial Tissues: Glandular Epithelium01:20

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The glandular epithelium is made of one or more epithelial cells modified to synthesize and secrete chemical substances. Glandular epithelia can be classified based on cell number. Unicellular glands have individual secretory cells scattered across the epithelial monolayer. In contrast, multicellular glands consist of a hollow tubular duct attached to the cluster of secretory cells located in the deep pockets.
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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Classification of Skin Cancer Lesions Using Explainable Deep Learning.

Muhammad Zia Ur Rehman1, Fawad Ahmed2, Suliman A Alsuhibany3

  • 1Department of Electrical Engineering, HITEC University Taxila, Taxila 47080, Pakistan.

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|September 23, 2022
PubMed
Summary
This summary is machine-generated.

Modified deep learning models improve skin cancer detection. The enhanced DenseNet201 model achieved 95.50% accuracy, aiding dermatologists in early diagnosis of skin cancer.

Keywords:
classificationdeep learningexplainable AI (XAI)skin cancertransfer learning

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Area of Science:

  • Medical imaging
  • Artificial intelligence in healthcare
  • Dermatology

Background:

  • Skin cancer is a major global health concern.
  • Traditional detection methods can be time-consuming.
  • Computer-aided diagnostic systems offer efficient solutions.

Purpose of the Study:

  • To enhance deep learning models for effective skin cancer detection.
  • To improve early diagnosis of both benign and malignant skin lesions.
  • To compare the performance of modified models against original pre-trained networks.

Main Methods:

  • Modification of pre-trained MobileNetV2 and DenseNet201 models.
  • Addition of three convolutional layers to each model.
  • Evaluation of detection performance for benign and malignant skin classes.

Main Results:

  • Modified models outperformed original pre-trained versions.
  • Modified DenseNet201 achieved 95.50% accuracy.
  • Modified DenseNet201 demonstrated high sensitivity (93.96%) and specificity (97.03%).

Conclusions:

  • The proposed Modified DenseNet201 model offers state-of-the-art performance for skin cancer detection.
  • Deep learning model enhancements can significantly improve diagnostic accuracy.
  • This approach can assist dermatologists in timely and accurate skin cancer diagnosis.