Jove
Visualize
Contact Us

Related Concept Videos

Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

19.2K
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,...
19.2K
Classification of Connective Tissues01:30

Classification of Connective Tissues

14.3K
The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
Connective Tissue Proper
Connective tissue proper is the most abundant class of connective tissues. As its name implies, it predominantly connects different tissues in the body. Depending on the cell types, ground substance, viscosity, and fiber types in the ECM, connective tissue proper is further categorized into loose and dense....
14.3K
Classification of Leukocytes01:30

Classification of Leukocytes

4.5K
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.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
4.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effect of magnetic field strength and segmentation variability on the reproducibility and repeatability of radiomic texture features in cardiovascular magnetic resonance parametric mapping.

The international journal of cardiovascular imaging·2025
Same author

Identification and characterization of two novel KCNH2 mutations contributing to long QT syndrome.

PloS one·2024
Same author

TIME- AND FREQUENCY-BASED INDEPENDENT EVALUATION OF QRST CANCELLATION TECHNIQUES FOR SINGLE-LEAD ELECTROCARDIOGRAMS DURING ATRIAL FIBRILLATION.

Annual Modeling and Simulation Conference (ANNSIM). Annual Modeling and Simulation Conference (Online)·2023
Same author

Optical Deformation of Biological Cells using Dual-Beam Laser Tweezer.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2022
Same author

An <i>in silico</i> hiPSC-Derived Cardiomyocyte Model Built With Genetic Algorithm.

Frontiers in physiology·2021
Same author

Role of the rapid delayed rectifier K<sup>+</sup> current in human induced pluripotent stem cells derived cardiomyocytes.

Archives of stem cell and therapy·2021
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Dec 6, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.2K

Convolutional Neural Network Based Breast Cancer Histopathology Image Classification.

Pascal Yamlome, Akwasi Darkwa Akwaboah, Aylin Marz

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    This study enhances breast cancer identification using improved Convolutional Neural Networks (CNNs) and whole-image analysis. The advanced CNN classifier achieved high accuracy, aiding in preliminary breast cancer diagnosis.

    More Related Videos

    Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
    05:33

    Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

    Published on: July 11, 2025

    612

    Related Experiment Videos

    Last Updated: Dec 6, 2025

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

    2.2K
    Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
    05:33

    Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

    Published on: July 11, 2025

    612

    Area of Science:

    • Medical Imaging
    • Computational Pathology
    • Artificial Intelligence in Oncology

    Background:

    • Breast cancer poses a significant global health challenge, necessitating advancements in diagnostic tools.
    • Computerized classification of histopathology images using Convolutional Neural Networks (CNNs) shows promise for breast cancer diagnosis.

    Purpose of the Study:

    • To enhance the performance of CNN-based classifiers for breast cancer histopathology image identification.
    • To investigate the efficacy of combining transfer learning, data augmentation, and whole-image training.

    Main Methods:

    • Utilized a modified CNN pre-trained on the ImageNet dataset.
    • Implemented high-resolution whole-image training and testing, avoiding conventional image patch extraction.
    • Integrated transfer learning techniques with data augmentation.

    Main Results:

    • Achieved significant performance improvement over previous studies on the BreakHis dataset.
    • Attained an average image-level accuracy of approximately 91% and patient-level scores up to 95%.

    Conclusions:

    • The proposed training techniques substantially improve CNN performance for breast cancer histopathology classification.
    • Enhanced CNN classification can support preliminary examination of tissue slides, aiding in breast cancer diagnosis.