Jove
Visualize
Contact Us
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 Concept Videos

Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

6.0K
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
6.0K
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

13.2K
Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
13.2K
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

5.7K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
5.7K
Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

15.7K
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,...
15.7K

You might also read

Related Articles

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

Sort by
Same author

Breaking Diagnostic Barriers: Vision Transformers Redefine Monkeypox Detection.

Diagnostics (Basel, Switzerland)·2025
Same author

Vision Transformers for Low-Quality Histopathological Images: A Case Study on Squamous Cell Carcinoma Margin Classification.

Diagnostics (Basel, Switzerland)·2025
Same author

Microscale Flow Control and Droplet Generation Using Arduino-Based Pneumatically-Controlled Microfluidic Device.

Biosensors·2024
Same author

Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer.

Cancers·2024
Same author

An Alternative Athlete Monitoring System Using Cost-Effective Inertial Sensing Instrumentation.

Journal of electrical engineering & technology·2023
Same author

Vision-Transformer-Based Transfer Learning for Mammogram Classification.

Diagnostics (Basel, Switzerland)·2023

Related Experiment Video

Updated: Sep 23, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K

Automatic Cancer Cell Taxonomy Using an Ensemble of Deep Neural Networks.

Se-Woon Choe1,2, Ha-Yeong Yoon3, Jae-Yeop Jeong3

  • 1Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea.

Cancers
|May 14, 2022
PubMed
Summary

This study introduces a deep learning method for automatic cancer cell type classification from microscopic images, significantly improving accuracy and addressing misidentification issues in pathology.

Keywords:
cancer cell taxonomyconvolutional neural networkdeep learningensemble approach

More Related Videos

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.3K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

Related Experiment Videos

Last Updated: Sep 23, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.0K
Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.3K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.3K

Area of Science:

  • Pathology
  • Computational Biology
  • Artificial Intelligence

Background:

  • Microscopic image analysis is crucial for disease diagnosis.
  • Mis-authentication of cancer cell lines due to human error is a significant challenge in pathology.
  • Accurate cell line identification is vital for reliable research and clinical outcomes.

Purpose of the Study:

  • To develop and evaluate a deep learning-based approach for the automatic classification of cancer cell types.
  • To improve the accuracy and reliability of cancer cell identification compared to traditional methods.
  • To investigate the impact of various deep learning model parameters on classification performance.

Main Methods:

  • Acquisition of 889 bright-field microscopic images from four cancer cell lines.
  • Segmentation and augmentation of individual cells to create an expanded dataset.
  • Application of deep transfer learning for accelerated cancer type classification.
  • Comparison of deep learning methods against traditional machine learning approaches.
  • Utilizing deep ensemble methods and analyzing network design choices (optimizer, learning rate, fine-tuning, data augmentation) via Wilcoxon signed-rank tests.

Main Results:

  • Deep learning methods demonstrated superior performance over traditional machine learning techniques.
  • Deep ensemble approaches achieved a classification accuracy of up to 97.735%, significantly outperforming individual deep learning models (p < 0.001).
  • Data augmentation and full network weight fine-tuning were identified as key factors improving individual convolutional neural network model performance.

Conclusions:

  • Deep learning offers a robust and accurate solution for automated cancer cell type classification from microscopic images.
  • Deep ensemble models provide enhanced classification performance and reliability in pathological analysis.
  • Optimizing deep learning model parameters, particularly through data augmentation and comprehensive fine-tuning, is crucial for maximizing accuracy in cancer cell identification.