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Related Experiment Video

Updated: Jun 23, 2025

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

Automatic classification of normal and abnormal cell division using deep learning.

Pablo Delgado-Rodriguez¹, Rodrigo Morales Sánchez¹, Elouan Rouméas-Noël²

¹Universidad Carlos III de Madrid, Leganes, Spain.

Scientific Reports

|June 20, 2024

Summary

This summary is machine-generated.

This study introduces an automatic mitosis classifier to distinguish normal from abnormal cell divisions caused by DNA damage. The deep learning model achieved high accuracy, aiding in the analysis of genomic stress effects on cell populations.

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Last Updated: Jun 23, 2025

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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

Cell Biology
Genomics
Bioinformatics

Background:

Cell segmentation and tracking methods are advancing, but struggle with abnormal events like DNA damage-induced cell divisions.
Genomic stress from radiation or drugs causes irregular cell behaviors, necessitating specialized analysis tools.

Purpose of the Study:

To develop an automatic classifier for categorizing mitosis as normal or abnormal.
To analyze cell cycle development under varying levels of radiation-induced genomic stress.

Main Methods:

Utilized deep learning architectures, specifically a ResNet50 backbone with a Long Short-Term Memory (LSTM) layer.
Trained and evaluated the classifier on mitosis image sequences from cell populations exposed to radiation.

Main Results:

The developed classifier achieved a high mean F1-score of 0.93 ± 0.06.
The ResNet50 with LSTM network demonstrated superior performance in classifying mitosis events.

Conclusions:

An effective automatic mitosis classifier for detecting abnormal cell divisions under genomic stress was created.
Future work includes integrating this classifier with cell tracking for phylogenetic tree construction to study population dynamics after genomic stress.