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Optimizing Blood Cell Segmentation In Hematological Analysis Using Advanced Image Processing Techniques

  • Unique Paper ID: 174507
  • Volume: 11
  • Issue: 10
  • PageNo: 4571-4576
  • Abstract:
  • Accurate identification and classification of RBC morphology are significant in hematological diagnosis, especially in poikilocytosis disorders. Microscopic examination requires time and is prone to human error. This article presents an image processing and Faster Region-Based Convolutional Neural Networks (Faster R-CNN)-based automatic system for segmentation and identification of blood cells. The proposed system uses a severity grading feature for classifying cases of poikilocytosis as mild, moderate, and severe based on clinically proven thresholds. The system is efficient in classifying RBCs and has an automatic diagnostic report feature, supporting improved clinical decision-making and patient outcome.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 10
  • PageNo: 4571-4576

Optimizing Blood Cell Segmentation In Hematological Analysis Using Advanced Image Processing Techniques

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