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Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

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Imaging Biological Samples with Optical Microscopy

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Flow Cytometry

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Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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Related Experiment Video

Updated: May 14, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

Adaptive elastic convolution-based YOLO for peripheral blood smear cell detection.

Neha Margret Issac1, Rajakumar K2

  • 1School of Computer Science and Engineering, Department of Analytics, Vellore Institute of Technology, Vellore, ‌‌Tamilnadu, India.

Plos One
|May 12, 2026
PubMed
Summary

Elastic YOLO (EYOLO) improves automated blood cell detection in peripheral blood smears, enhancing accuracy for diagnosing hematologic conditions. This AI system offers rapid analysis for telemedicine and computer-assisted workflows.

Related Experiment Videos

Last Updated: May 14, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

Area of Science:

  • Hematology
  • Medical Imaging
  • Computer Vision

Background:

  • Peripheral blood smear analysis is crucial for diagnosing hematologic conditions.
  • Automated systems struggle with diverse cell types and multi-class detection in these images.

Purpose of the Study:

  • To develop an AI model for morphology-aware detection of red blood cells, white blood cells, and platelets.
  • To improve the accuracy and efficiency of automated peripheral blood smear analysis.

Main Methods:

  • Proposed Elastic YOLO (EYOLO), an extension of the YOLO object detection framework.
  • Utilized elastic adaptive convolutions to dynamically adjust to cell variations.
  • Trained and evaluated on a clinician-annotated dataset.

Main Results:

  • Elastic YOLO achieved high performance metrics (mAP@0.5: 94.7%, mAP@0.5:0.95: 87.8%).
  • Outperformed baseline YOLOv5 and other recent detection architectures.
  • Demonstrated fast inference speeds (up to 78 FPS) on high-performance GPUs.

Conclusions:

  • Elastic YOLO offers a robust solution for automated peripheral blood smear analysis.
  • The system can support computer-assisted hematology and remote screening via telemedicine.
  • Dynamic adaptation to cell morphology enhances detection accuracy across varied conditions.