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Volume 22, Issue 3 (12-2025)
JSDP 2025, 22(3): 59-76 |
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eslamifar O, soltani M, Rastegar Fatemi S M. Identification, detection and classification and of multiclass heterogeneous blood cell series based on the council algorithm and aggregation of tissue descriptors. JSDP 2025; 22 (3) : 4
URL: http://jsdp.rcisp.ac.ir/article-1-1393-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1393-en.html
Assistant Professor Department of Electrical Engineering Khomeinishahr Branch, Islamic Azad University, Isfahan , Iran
Abstract: (428 Views)
Understanding the structural and morphological characteristics of blood cells plays a crucial role in the early diagnosis and treatment of hematological disorders. Manual inspection of blood smears under a microscope is still the standard approach in many laboratories; however, this process is subjective, time-consuming, and highly dependent on the expertise of the hematologist. To overcome these limitations, the present study introduces an intelligent hybrid framework for multiclass classification of heterogeneous blood cells based on the integration of deep learning and metaheuristic optimization techniques.
In the proposed approach, the wavelet coefficients of microscopic images are first extracted to capture discriminative frequency-domain features. These coefficients are then fed into a YOLO-based convolutional neural network to detect candidate cell regions and identify spatial characteristics. A customized CNN architecture is subsequently employed for hierarchical feature learning, while a Golden Eagle Optimization (GEO) algorithm is utilized to perform feature selection and dimensionality reduction by eliminating redundant and less informative attributes.
To achieve robust decision-making, three classical classifiers Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbors (KNN) are combined through a weighted voting ensemble strategy. The model was trained and validated on a dataset consisting of microscopic images of five major white blood cell types: lymphocytes, monocytes, eosinophils, basophils, and neutrophils. Quantitative evaluation was performed using precision, recall, F1-score, and accuracy metrics.
Experimental results demonstrate that the proposed CNN GEO ensemble model achieves an overall accuracy of 95.7% and an average F1-score of 94.9%, outperforming comparable state-of-the-art methods such as CNN+SVM, PSO+KNN, and VGG-16 in both accuracy and computational efficiency. The findings highlight the capability of the proposed system to accurately distinguish among multiple blood cell categories, thereby providing a reliable and automated decision-support tool for early hematological diagnosis. Future work will focus on expanding the dataset and integrating domain adaptation mechanisms to further enhance cross-laboratory generalization.
In the proposed approach, the wavelet coefficients of microscopic images are first extracted to capture discriminative frequency-domain features. These coefficients are then fed into a YOLO-based convolutional neural network to detect candidate cell regions and identify spatial characteristics. A customized CNN architecture is subsequently employed for hierarchical feature learning, while a Golden Eagle Optimization (GEO) algorithm is utilized to perform feature selection and dimensionality reduction by eliminating redundant and less informative attributes.
To achieve robust decision-making, three classical classifiers Decision Tree (DT), Naïve Bayes (NB), and K-Nearest Neighbors (KNN) are combined through a weighted voting ensemble strategy. The model was trained and validated on a dataset consisting of microscopic images of five major white blood cell types: lymphocytes, monocytes, eosinophils, basophils, and neutrophils. Quantitative evaluation was performed using precision, recall, F1-score, and accuracy metrics.
Experimental results demonstrate that the proposed CNN GEO ensemble model achieves an overall accuracy of 95.7% and an average F1-score of 94.9%, outperforming comparable state-of-the-art methods such as CNN+SVM, PSO+KNN, and VGG-16 in both accuracy and computational efficiency. The findings highlight the capability of the proposed system to accurately distinguish among multiple blood cell categories, thereby providing a reliable and automated decision-support tool for early hematological diagnosis. Future work will focus on expanding the dataset and integrating domain adaptation mechanisms to further enhance cross-laboratory generalization.
Article number: 4
Type of Study: Research |
Subject:
Paper
Received: 2023/08/17 | Accepted: 2025/08/10 | Published: 2025/12/19 | ePublished: 2025/12/19
Received: 2023/08/17 | Accepted: 2025/08/10 | Published: 2025/12/19 | ePublished: 2025/12/19
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