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Related Experiment Video

Updated: Oct 2, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Deep Learning-Enabled Technologies for Bioimage Analysis.

Fazle Rabbi¹, Sajjad Rahmani Dabbagh^1,2,3, Pelin Angin⁴

¹Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey.

|February 25, 2022

Summary

This summary is machine-generated.

Deep learning (DL) enhances cell morphology quantification in medicine. This review covers DL applications in embryology, diagnostics, and disease prediction, showcasing its potential in biomedical fields.

Keywords:

bioimage quantification cancer diagnosis cell morphology classification deep learning machine learning

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Area of Science:

Biomedical Image Analysis
Machine Learning
Cellular Biology

Background:

Deep learning (DL), a subset of machine learning (ML), shows significant promise in improving biomedical and clinical workflows.
Cellular morphology quantification is a key area benefiting from DL advancements.

Purpose of the Study:

To explain fundamental DL concepts.
To review emerging DL applications in cell morphology quantification across various medical fields.

Main Methods:

Review of existing literature on DL applications in cell morphology.
Explanation of core DL principles relevant to image analysis.

Main Results:

DL is effectively applied in embryology for cell analysis.
DL aids in point-of-care ovulation testing and fetal heart pregnancy prediction.
DL assists in cancer diagnostics through histology image classification.
DL shows potential in diagnosing kidney diseases like autosomal polycystic kidney disease and chronic kidney diseases.

Conclusions:

DL offers powerful tools for advancing cell morphology quantification in diverse biomedical applications.
The integration of DL is transforming diagnostics and predictive capabilities in healthcare.