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Related Concept Videos

Classification of Epithelial Tissues: Stratified Epithelium01:29

Classification of Epithelial Tissues: Stratified Epithelium

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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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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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Assessment of the Mouth01:26

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A thorough mouth assessment, including inspection and palpation of the lips, gums, tongue, tonsils, uvula, and pharynx, is crucial in detecting potential health issues. Diseases ranging from oral cancer to systemic conditions like diabetes could be identified early through careful oral examination. This article provides a detailed guide on conducting a comprehensive mouth assessment.
Mouth Inspection
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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.
Based on the number of cell layers,...
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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.
Because of the thinness of the cells, simple squamous epithelium is present where the rapid passage of chemical compounds is observed. For example, the endothelium that lines the capillaries and vessels...
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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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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition
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Textural pattern classification for oral squamous cell carcinoma.

T Y Rahman1, L B Mahanta1, C Chakraborty2

  • 1Centre for Computational and Numerical Sciences Division, Institute of Advanced Study in Science and Technology, Guwahati, Assam, India.

Journal of Microscopy
|August 3, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a computer-aided method for oral cancer diagnosis using histological images. The approach achieved 100% accuracy in identifying malignant cells from squamous cell carcinoma slides.

Keywords:
BiopsyGLCMPCASCCSVMhistogramoral cancert-testtexture

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Area of Science:

  • Oncology
  • Digital Pathology
  • Medical Imaging Analysis

Background:

  • Oral cancer has the highest global incidence but lacks extensive research.
  • Computer-aided analysis of oral cancer pathological slides aids diagnosis and treatment.
  • Previous research has explored oral submucous fibrosis using similar methods.

Purpose of the Study:

  • To develop an approach for analyzing abnormalities in squamous cell carcinoma histological slides.
  • To utilize textural features for automated oral cancer diagnosis.
  • To improve the accuracy and efficiency of oral cancer detection.

Main Methods:

  • Extraction of textural features from biopsy images using histogram and grey-level co-occurrence matrix (GLCM) approaches.
  • Analysis of histological slides containing normal and malignant oral cells.
  • Application of a linear support vector machine (SVM) classifier for automated diagnosis.

Main Results:

  • The developed method successfully extracted relevant textural features from oral cancer slides.
  • The linear support vector machine classifier demonstrated high performance in distinguishing between normal and malignant cells.
  • The automated diagnosis system achieved 100% accuracy in classifying oral squamous cell carcinoma.

Conclusions:

  • Textural feature analysis of histological slides is a promising approach for oral cancer detection.
  • Computer-aided diagnosis using machine learning, specifically SVM, can accurately diagnose oral cancer.
  • This method offers a potential tool for improving early detection and management of oral cancer.