Efficient white blood cell identification with hybrid inception-xception network

Abstract

White blood cells (WBCs) are crucial microscopic defenders of the human immune system in combating transmittable conditions triggered by germs, infections, and various other human pathogens. Timely and appropriate WBC detection and classification are decisive for comprehending the immune system’s standing and its feedback to various pathologies, assisting in diagnosing and monitoring illness. Nevertheless, the manual classification of WBCs is strenuous, extensive, and prone to errors, while automated approaches can be cost-prohibitive. Within artificial intelligence, deep learning (DL) approaches have become an appealing option for automating WBC recognition. The existing DL techniques for WBC classification face several limitations and computational difficulties, such as overfitting, limited scalability, and design complexity, often battling with function variety in WBC images and requiring considerable computational resources. This research study recommends an ingenious hybrid inception-xception Convolutional Semantic network (CNN) designed to deal with constraints in existing DL versions. The proposed network incorporates inception and depth-separable convolution layers to successfully catch attributes across many ranges, therefore minimizing concerns related to model complexity and overfitting. In contrast to traditional CNN designs, the proposed network lessens the layers made use of and increases their function removal capacities, hence enhancing the performance of WBC classification, which needs a wide variety of attribute abilities. Furthermore, the proposed model was trained, validated and tested on three popular and widely recognized datasets, namely, Leukocyte Images for Segmentation and Classification (LISC), Blood Cell Count and Detection (BCCD), and Microscopic PBS (PBS-HCB), where it demonstrates the generalization and robustness and superiority of our proposed model. The model depicted an outstanding average accuracy rate of 99.25%, 99.65%, and 98.6% on a five-fold cross-validation test for the respective datasets, surpassing existing models as detailed. The model’s robustness and superior performance, validated across diverse datasets, underscore its potential as an advanced tool for accurate and efficient WBC classification in medical diagnostics.

Full text

Vol.:(0123456789)
The Journal of Supercomputing (2024) 80:25155–25187
https://doi.org/10.1007/s11227-024-06405-1
Efficient white blood cell identification withhybrid
inception‑xception network
RadhwanA.A.Saleh1,2· MustafaGhaleb3· WasswaShak4· H.MetinERTUNÇ1
Accepted: 30 July 2024 / Published online: 7 August 2024
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
2024
Abstract
White blood cells (WBCs) are crucial microscopic defenders of the human immune
system in combating transmittable conditions triggered by germs, infections,
and various other human pathogens. Timely and appropriate WBC detection and
classification are decisive for comprehending the immune system’s standing and
its feedback to various pathologies, assisting in diagnosing and monitoring illness.
Nevertheless, the manual classification of WBCs is strenuous, extensive, and prone
to errors, while automated approaches can be cost-prohibitive. Within artificial
intelligence, deep learning (DL) approaches have become an appealing option for
automating WBC recognition. The existing DL techniques for WBC classification
face several limitations and computational difficulties, such as overfitting, limited
scalability, and design complexity, often battling with function variety in WBC
images and requiring considerable computational resources. This research study
recommends an ingenious hybrid inception-xception Convolutional Semantic
network (CNN) designed to deal with constraints in existing DL versions. The
proposed network incorporates inception and depth-separable convolution layers
to successfully catch attributes across many ranges, therefore minimizing concerns
related to model complexity and overfitting. In contrast to traditional CNN designs,
the proposed network lessens the layers made use of and increases their function
removal capacities, hence enhancing the performance of WBC classification,
which needs a wide variety of attribute abilities. Furthermore, the proposed model
was trained, validated and tested on three popular and widely recognized datasets,
namely, Leukocyte Images for Segmentation and Classification (LISC), Blood
Cell Count and Detection (BCCD), and Microscopic PBS (PBS-HCB), where it
demonstrates the generalization and robustness and superiority of our proposed
model. The model depicted an outstanding average accuracy rate of 99.25%, 99.65%,
and 98.6% on a five-fold cross-validation test for the respective datasets, surpassing
existing models as detailed. The model’s robustness and superior performance,
validated across diverse datasets, underscore its potential as an advanced tool for
accurate and efficient WBC classification in medical diagnostics.
Extended author information available on the last page of the article
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