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Related Concept Videos

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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Related Experiment Video

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Stacking Ensemble Technique for Classifying Breast Cancer.

Hyunjin Kwon1, Jinhyeok Park1, Youngho Lee2

  • 1Department of IT Convergence Engineering, Gachon University, Seongnam, Korea.

Healthcare Informatics Research
|November 29, 2019
PubMed
Summary

This study identified the best machine learning models for breast cancer classification using stacking ensembles. Gradient Boosted Models (GBM) and Generalized Linear Models (GLM) showed superior performance as meta-learners for early breast cancer detection.

Keywords:
Breast CancerClassificationData AnalysisMachine LearningMedical Informatics

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Area of Science:

  • Oncology
  • Machine Learning
  • Biostatistics

Background:

  • Breast cancer is a significant health concern for Korean women, necessitating effective early detection and treatment strategies.
  • Early diagnosis of breast cancer is crucial due to its association with severe emotional and physical distress.
  • Machine learning offers potential as a supportive tool for accurate breast cancer classification.

Purpose of the Study:

  • To identify the optimal meta-learner model within a stacking ensemble for breast cancer classification.
  • To evaluate the performance of various machine learning models when used as both base and meta-learners.
  • To determine the most effective ensemble configuration for improving breast cancer detection accuracy.

Main Methods:

  • Employed a stacking ensemble framework utilizing Gradient Boosted Models (GBM), Distributed Random Forest (DRF), Generalized Linear Models (GLM), and Deep Neural Networks (DNN).
  • Each model served as a base learner, and subsequently, as a meta-learner to assess its performance in the ensemble.
  • Compared the predictive accuracy and error rates of different meta-learner configurations on breast cancer datasets.

Main Results:

  • Using the Gradient Boosted Model (GBM) as a meta-learner resulted in superior accuracy for breast cancer data classification.
  • The Generalized Linear Model (GLM) as a meta-learner demonstrated a low root-mean-squared error, indicating high precision.
  • Ensemble models with specific meta-learners outperformed single classifier models in performance.

Conclusions:

  • Stacking ensembles with GBM and GLM as meta-learners are effective supporting tools for breast cancer classification.
  • The selection of an appropriate meta-learner significantly enhances the performance of machine learning models in breast cancer detection.
  • These findings support the use of advanced machine learning techniques for improving early breast cancer diagnosis and patient outcomes.