A Hybrid Deep Learning Framework for Accurate Blood Cell Counting and Classification Using Advanced Segmentation and Feature Extraction

DHANYA.D; Dr. M.R. GEETHA

doi:10.65379/tpsn2013/ijaemsv01i01p2

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A Hybrid Deep Learning Framework for Accurate Blood Cell Counting and Classification Using Advanced Segmentation and Feature Extraction

Author(s): DHANYA.D, Dr. M.R. GEETHA

Volume RegistryVolume 1

Issue PeriodIssue 01

Published Date15 Apr 2025

DOI Identifier🔗 10.65379/tpsn2013/ijaemsv01i01p2

Abstract

Blood cell counting is a crucial process in medical diagnostics for identifying and classifying red blood cells (RBCs), white blood cells (WBCs), and platelets. Accurate counting and classification are challenging due to overlapping cells, noise, and variations in cell shapes. To overcome these issues, we propose a robust approach integrating Segmentation using Canny Edge Detector and Watershed Segmentation. For Feature Extraction, we employ Texture-Based Features using Local Binary Patterns (LBP). Finally, Counting and Classification are performed using Convolutional Neural Networks (CNN). This combined method aims to enhance accuracy and efficiency in automated blood cell analysis.

Keywords

red blood cells white blood cells Canny Edge Detector Watershed Segmentation Convolutional Neural Networks

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DHANYA.D and Dr. M.R. GEETHA, "A Hybrid Deep Learning Framework for Accurate Blood Cell Counting and Classification Using Advanced Segmentation and Feature Extraction," Int. J. Adv. Eng. Manag. Syst., vol. 1, no. 1, pp. 29-40, 2025. doi: 10.65379/tpsn2013/ijaemsv01i01p2.

A Hybrid Deep Learning Framework for Accurate Blood Cell Counting and Classification Using Advanced Segmentation and Feature Extraction

Abstract

Keywords

References (20)

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