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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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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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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: 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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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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Pap Smear Images Classification Using Machine Learning: A Literature Matrix.

Nur Ain Alias1, Wan Azani Mustafa1,2, Mohd Aminudin Jamlos3

  • 1Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Perlis, Malaysia.

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Summary
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This study reviews machine learning applications for automated cervical cancer classification. It highlights the need for accurate, accessible screening methods to improve early diagnosis and patient outcomes.

Keywords:
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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cervical cancer is a global health concern, ranking as the seventh most frequent cancer worldwide and fourth most prevalent among women.
  • Early diagnosis through methods like Pap smears is crucial for improving clinical outcomes.
  • While AI enhances medical imaging, cost and resource limitations hinder AI-based cervical cancer screening systems.

Purpose of the Study:

  • To systematically review and analyze current research on machine learning for cervical cell classification.
  • To assess past approaches in automation-assisted cervical cancer screening.
  • To identify advancements and challenges in AI-driven cervical cancer diagnosis.

Main Methods:

  • Systematic literature review of studies indexed by Scopus and Web of Science.
  • Analysis of machine learning applications for cervical cell classification.
  • Assessment of automated cervical cancer classification methods published until October 2022.

Main Results:

  • Identified various machine learning approaches for classifying cervical cells.
  • Evaluated the effectiveness of different algorithms in automated cervical cancer detection.
  • Highlighted the progress in AI-based cell image analysis for screening.

Conclusions:

  • Machine learning shows significant potential for automating cervical cancer classification.
  • Further research is needed to overcome resource constraints for widespread AI implementation in screening.
  • Developing cost-effective AI solutions can enhance early cervical cancer detection globally.