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Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

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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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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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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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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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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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Breast Tumor Classification Using an Ensemble Machine Learning Method.

Adel S Assiri1, Saima Nazir2, Sergio A Velastin3,4,5

  • 1College of Business, King Khalid University, Abha 62529, Saudi Arabia.

Journal of Imaging
|August 30, 2021
PubMed
Summary

This study introduces an ensemble artificial intelligence (AI) model for breast cancer detection. The AI model achieved 99.42% accuracy, outperforming existing methods for breast cancer classification.

Keywords:
breast cancer tumorclassificationmajority-based voting mechanismmultilayer perceptron learning networksimple logistic regressionstochastic gradient descent learningwisconsin breast cancer dataset

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Artificial Intelligence

Background:

  • Breast cancer remains a leading cause of mortality in women globally.
  • Accurate and early detection of breast cancer is critical for improving patient outcomes.
  • Artificial intelligence (AI) offers promising avenues for enhancing breast cancer diagnostic capabilities.

Purpose of the Study:

  • To propose and evaluate an ensemble classification mechanism for breast cancer detection using AI.
  • To compare the performance of various machine learning algorithms on the Wisconsin Breast Cancer Dataset (WBCD).
  • To determine the optimal voting strategy for an ensemble model focused on minimizing false negatives.

Main Methods:

  • Evaluated multiple state-of-the-art machine learning classifiers on the WBCD.
  • Selected the top three classifiers based on the F3 score, prioritizing recall (minimizing false negatives).
  • Implemented an ensemble classification using a majority voting mechanism (hard voting) with logistic regression, support vector machines, and multilayer perceptron networks.

Main Results:

  • The ensemble classification model using hard (majority-based) voting achieved a high accuracy of 99.42%.
  • This performance surpassed existing state-of-the-art algorithms on the WBCD.
  • Different soft voting methods (average, product, max, min probabilities) were evaluated but showed lower performance than hard voting.

Conclusions:

  • An ensemble classification approach, particularly with majority-based voting, demonstrates superior performance in breast cancer detection.
  • The developed AI system offers a highly accurate method for breast cancer classification.
  • This research highlights the potential of ensemble AI models in improving diagnostic accuracy for critical diseases like breast cancer.