*Result*: Attention-Based Dual-Path Deep Learning for Blood Cell Image Classification Using ConvNeXt and Swin Transformer.
Title:
Attention-Based Dual-Path Deep Learning for Blood Cell Image Classification Using ConvNeXt and Swin Transformer.
Authors:
Kılıç Ş; Department of Software Engineering, Kayseri University, Kayseri, Turkey. safakkilic@kayseri.edu.tr.
Source:
Journal of imaging informatics in medicine [J Imaging Inform Med] 2026 Feb; Vol. 39 (1), pp. 564-582. Date of Electronic Publication: 2025 Apr 29.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: [Cham, Switzerland] : Springer Nature, [2024]-
MeSH Terms:
References:
Girdhar A, Kapur H, Kumar V, Kaur M, Singh D, Damasevicius R. Effect of COVID-19 outbreak on urban health and environment. Air Quality, Atmosphere & Health. 2021;14:389–397. (PMID: 10.1007/s11869-020-00944-1)
Funamizu H, Aizu Y. Three-dimensional quantitative phase imaging of blood coagulation structures by optical projection tomography in flow cytometry using digital holographic microscopy. Journal of biomedical optics. 2019;24(3):031012–031012.
Hunder GG, Pierre RV. In vivo LE cell formation in synovial fluid. Arthritis & Rheumatism: Official Journal of the American College of Rheumatology. 1970;13(4):448–454. (PMID: 10.1002/art.1780130415)
Fu X, Fu M, Li Q, Peng X, Lu J, Fang F, et al. Morphogo: an automatic bone marrow cell classification system on digital images analyzed by artificial intelligence. Acta Cytologica. 2020;64(6):588–596. (PMID: 3272195310.1159/000509524)
Ding L, Ren H. Segmentation algorithm of medical exercise rehabilitation image based on hfcnn and iot. IEEE Access. 2019;7:160829–160844. (PMID: 10.1109/ACCESS.2019.2950960)
Chen J, Frey EC, He Y, Segars WP, Li Y, Du Y. Transmorph: Transformer for unsupervised medical image registration. Medical image analysis. 2022;82:102615. (PMID: 36156420999948310.1016/j.media.2022.102615)
Bay H, Tuytelaars T, Van Gool L. Surf: Speeded up robust features. In: Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I 9. Springer; 2006. p. 404–417.
Lowe DG. Distinctive image features from scale-invariant keypoints. International journal of computer vision. 2004;60:91–110. (PMID: 10.1023/B:VISI.0000029664.99615.94)
Kobayashi T, Hidaka A, Kurita T. Selection of histograms of oriented gradients features for pedestrian detection. In: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II 14. Springer; 2008. p. 598–607.
Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics. 1973;(6):610–621. (PMID: 10.1109/TSMC.1973.4309314)
Acevedo A, Alférez S, Merino A, Puigví L, Rodellar J. Recognition of peripheral blood cell images using convolutional neural networks. ComputMethodsProgramsBiomed. 2019;180:105020.
Wang Y, Cao Y. Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation. MedPhys. 2020;47(1):142–151.
Sahlol A, Kollmannsberger P, Ewees A. Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. SciRep. 2020;10(1):1–11.
Rawat J, Singh A, Bhadauria H, Virmani J, Devgun JS. Application of ensemble artificial neural network for the classification of white blood cells using microscopic blood images. International Journal of Computational Systems Engineering. 2018;4:202. (PMID: 10.1504/IJCSYSE.2018.091407)
Sahlol AT, Kollmannsberger P, Ewees AA. Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Scientific Reports. 2020;10.
Yao J, Huang X, Wei M, Han W, Xu X, Wang R, et al. High-efficiency classification of white blood cells based on object detection. Journal of Healthcare Engineering. 2021;2021:1–11.
Singh I, Singh NP, Singh HP, Bawankar S, Ngom A. Blood cell types classification using cnn. Bioinformatics and Biomedical Engineering. 2020;p. 727–738.
Khan S, Sajjad M, Hussain T, Ullah A, Imran AS. A review on traditional machine learning and deep learning models for WBCs classification in blood smear images. Ieee Access. 2020;9:10657–10673. (PMID: 10.1109/ACCESS.2020.3048172)
Deshpande NM, Gite S, Aluvalu R. A review of microscopic analysis of blood cells for disease detection with AI perspective. PeerJ Computer Science. 2021;7:e460. (PMID: 33981834808042710.7717/peerj-cs.460)
Ozdemir C, Dogan Y, Kaya Y. RGB-Angle-Wheel: A new data augmentation method for deep learning models. Knowledge-Based Systems. 2024;291:111615. (PMID: 10.1016/j.knosys.2024.111615)
Toğaçar M, Ergen B, Sertkaya ME. Subclass separation of white blood cell images using convolutional neural network models. Elektronika Ir Elektrotechnika. 2019;25:63–68. (PMID: 10.5755/j01.eie.25.5.24358)
Wang Q, Wang J, Zhou M, Li Q, Wen Y, Chu J. A 3D attention networks for classification of white blood cells from microscopy hyperspectral images. Optics & Laser Technology. 2021;139:106931. (PMID: 10.1016/j.optlastec.2021.106931)
Basnet J, Alsadoon A, Prasad P, Aloussi SA, Alsadoon OH. A novel solution of using deep learning for white blood cells classification: Enhanced loss function with regularization and weighted loss (ELFRWL). Neural Processing Letters. 2020;52:1517–1553. (PMID: 10.1007/s11063-020-10321-9)
Jiang M, Liu C, Qin F, Du L, Zhang M. White blood cells classification with deep convolutional neural networks. International Journal of Pattern Recognition and Artificial Intelligence. 2018;32:1857006. (PMID: 10.1142/S0218001418570069)
Yao X, Sun K, Bu X, Zhao C, Jin Y. Classification of white blood cells using weighted optimized deformable convolutional neural networks. Artificial Cells, Nanomedicine, and Biotechnology. 2021;49(1):147–155. (PMID: 3353365610.1080/21691401.2021.1879823)
Khan A, Eker A, Chefranov A, Demirel H. White blood cell type identification using multi-layer convolutional features with an extreme-learning machine. Biomedical Signal Processing and Control. 2021;69:102932. (PMID: 10.1016/j.bspc.2021.102932)
Çınar A, Tuncer SA. Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM. SN Applied Sciences. 2021;3:1–11. (PMID: 10.1007/s42452-021-04485-9)
Özyurt F. A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine. Soft Computing. 2020;24(11):8163–8172. (PMID: 10.1007/s00500-019-04383-8)
Patil A, Patil M, Birajdar G. White blood cells image classification using deep learning with canonical correlation analysis. Irbm. 2021;42(5):378–389. (PMID: 10.1016/j.irbm.2020.08.005)
Baghel N, Verma U, Nagwanshi KK. WBCs-Net: Type identification of white blood cells using convolutional neural network. Multimedia Tools and Applications. 2022;81(29):42131–42147. (PMID: 10.1007/s11042-021-11449-z)
Erten M, Barua PD, Dogan S, Tuncer T, Tan RS, Acharya U. ConcatNeXt: An automated blood cell classification with a new deep convolutional neural network. Multimedia Tools and Applications. 2024;p. 1–19.
Razzak MI, Naz S. Microscopic Blood Smear Segmentation and Classification Using Deep Contour Aware CNN and Extreme Machine Learning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2017. p. 801–807.
Yu W, Chang J, Yang C, Zhang L, Shen H, Xia Y, et al. Automatic classification of leukocytes using deep neural network. 2017 IEEE 12th International Conference on ASIC (ASICON). 2017;p. 1041–1044. Available from: https://api.semanticscholar.org/CorpusID:28326840 .
Liang G, Hong H, Xie W, Zheng L. Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification. IEEE Access. 2018;6:36188–36197. Available from: https://api.semanticscholar.org/CorpusID:49870021 .
Hegde RB, Prasad K, Hebbar H, Singh BMK. Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images. Biocybernetics and Biomedical Engineering. 2019;39:382–392. (PMID: 10.1016/j.bbe.2019.01.005)
El-Seoud SA, Siala MH, McKee G. Detection and classification of white blood cells through deep learning techniques. International Journal of Online and Biomedical Engineering (iJOE). 2020;16:94. (PMID: 10.3991/ijoe.v16i15.15481)
Banik PP, Saha R, Kim K. An automatic nucleus segmentation and cnn model based classification method of white blood cell. Expert Systems With Applications. 2020;149:113211. (PMID: 10.1016/j.eswa.2020.113211)
Basnet J, Alsadoon A, Prasad PWC, Aloussi SA, Alsadoon OH. A novel solution of using deep learning for white blood cells classification: enhanced loss function with regularization and weighted loss (elfrwl). Neural Processing Letters. 2020;52:1517–1553. (PMID: 10.1007/s11063-020-10321-9)
Baydilli YY, Atila Ü. Classification of white blood cells using capsule networks. Computerized Medical Imaging and Graphics. 2020;80:101699. (PMID: 3200008710.1016/j.compmedimag.2020.101699)
Kutlu H, Avci E, Özyurt F. White blood cells detection and classification based on regional convolutional neural networks. Medical hypotheses. 2020;135:109472. (PMID: 3176024810.1016/j.mehy.2019.109472)
Cheuque C, Querales M, León R, Salas R, Torres R. An efficient multi-level convolutional neural network approach for white blood cells classification. Diagnostics. 2022;12(2):248. (PMID: 35204339887131910.3390/diagnostics12020248)
Girdhar A, Kapur H, Kumar V. Classification of white blood cell using convolution neural network. Biomedical Signal Processing and Control. 2022;71:103156. (PMID: 10.1016/j.bspc.2021.103156)
Chen S, Tan X, Wang B, Lu H, Hu X, Fu Y. Reverse attention-based residual network for salient object detection. IEEE Transactions on Image Processing. 2020;29:3763–3776. (PMID: 10.1109/TIP.2020.2965989)
Landeros C, Oh J, Weissleder R, Lee H. Deep learning pipeline for automated cell profiling from cyclic imaging. Scientific Reports. 2024;14(1):23600. (PMID: 393849071146478910.1038/s41598-024-74597-w)
Cakir S, Kahraman DC, Cetin-Atalay R, Cetin AE. Contrast enhancement of microscopy images using image phase information. IEEE Access. 2018;6:3839–3850. (PMID: 10.1109/ACCESS.2018.2796646)
Heo YJ, Lee D, Kang J, Lee K, Chung WK. Real-time image processing for microscopy-based label-free imaging flow cytometry in a microfluidic chip. Scientific reports. 2017;7(1):11651. (PMID: 28912565559953510.1038/s41598-017-11534-0)
Schapiro D, Sokolov A, Yapp C, Chen YA, Muhlich JL, Hess J, et al. MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nature methods. 2022;19(3):311–315. (PMID: 3482447710.1038/s41592-021-01308-y)
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. ConvNeXt: A ConvNet for the 2020s. arXiv preprint arXiv:2201.03545 . 2022;.
Erdogan A. ConvNeXt - Next Generation of Convolutional Networks. Medium; 2023. Accessed: 2025-01-01. Available from: https://medium.com/@atakanerdogan305/convnext-next-generation-of-convolutional-networks-325607a08c46 .
Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020. p. 11534–11542.
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV); 2021. p. 10012–10022.
Tsang SH. Review: Swin Transformer. Medium; 2021. Accessed: 2025-01-01. Available from: https://sh-tsang.medium.com/review-swin-transformer-3438ea335585 .
Research M. Swin Transformer. GitHub; 2021. https://github.com/microsoft/Swin-Transformer .
Dai Y, Gieseke F, Oehmcke S, Wu Y, Barnard K. Attentional Feature Fusion. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision; 2021. p. 3560–3569.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015. p. 2818–2826.
Boldú L, Merino A, Acevedo A, Molina A, Rodellar J. A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images. Computer Methods and Programs in Biomedicine. 2021;202:105999. (PMID: 3361814510.1016/j.cmpb.2021.105999)
Baby D, Devaraj SJ, et al. Leukocyte classification based on transfer learning of VGG16 features by K-nearest neighbor classifier. In: 2021 3rd International Conference on Signal Processing and Communication (ICPSC). IEEE; 2021. p. 252–256.
Sen B, Ganesh A, Bhan A, Dixit S. Deep Learning based diagnosis of sickle cell anemia in human RBC. In: 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM). IEEE; 2021. p. 526–529.
Sheng B, Zhou M, Hu M, Li Q, Sun L, Wen Y. A blood cell dataset for lymphoma classification using faster R-CNN. Biotechnology & Biotechnological Equipment. 2020;34(1):413–420. (PMID: 10.1080/13102818.2020.1765871)
Acevedo A, Merino A, Alférez S, Molina Á, Boldú L, Rodellar J. A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data in brief. 2020;30.
Sharma S, Gupta S, Gupta D, Juneja S, Gupta P, Dhiman G, et al. Deep learning model for the automatic classification of white blood cells. Computational Intelligence and Neuroscience. 2022;2022.
Huang Q, Li W, Zhang B, Li Q, Tao R, Lovell NH. Blood cell classification based on hyperspectral imaging with modulated Gabor and CNN. IEEE journal of biomedical and health informatics. 2019;24(1):160–170. (PMID: 3089225610.1109/JBHI.2019.2905623)
Funamizu H, Aizu Y. Three-dimensional quantitative phase imaging of blood coagulation structures by optical projection tomography in flow cytometry using digital holographic microscopy. Journal of biomedical optics. 2019;24(3):031012–031012.
Hunder GG, Pierre RV. In vivo LE cell formation in synovial fluid. Arthritis & Rheumatism: Official Journal of the American College of Rheumatology. 1970;13(4):448–454. (PMID: 10.1002/art.1780130415)
Fu X, Fu M, Li Q, Peng X, Lu J, Fang F, et al. Morphogo: an automatic bone marrow cell classification system on digital images analyzed by artificial intelligence. Acta Cytologica. 2020;64(6):588–596. (PMID: 3272195310.1159/000509524)
Ding L, Ren H. Segmentation algorithm of medical exercise rehabilitation image based on hfcnn and iot. IEEE Access. 2019;7:160829–160844. (PMID: 10.1109/ACCESS.2019.2950960)
Chen J, Frey EC, He Y, Segars WP, Li Y, Du Y. Transmorph: Transformer for unsupervised medical image registration. Medical image analysis. 2022;82:102615. (PMID: 36156420999948310.1016/j.media.2022.102615)
Bay H, Tuytelaars T, Van Gool L. Surf: Speeded up robust features. In: Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I 9. Springer; 2006. p. 404–417.
Lowe DG. Distinctive image features from scale-invariant keypoints. International journal of computer vision. 2004;60:91–110. (PMID: 10.1023/B:VISI.0000029664.99615.94)
Kobayashi T, Hidaka A, Kurita T. Selection of histograms of oriented gradients features for pedestrian detection. In: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II 14. Springer; 2008. p. 598–607.
Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics. 1973;(6):610–621. (PMID: 10.1109/TSMC.1973.4309314)
Acevedo A, Alférez S, Merino A, Puigví L, Rodellar J. Recognition of peripheral blood cell images using convolutional neural networks. ComputMethodsProgramsBiomed. 2019;180:105020.
Wang Y, Cao Y. Human peripheral blood leukocyte classification method based on convolutional neural network and data augmentation. MedPhys. 2020;47(1):142–151.
Sahlol A, Kollmannsberger P, Ewees A. Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. SciRep. 2020;10(1):1–11.
Rawat J, Singh A, Bhadauria H, Virmani J, Devgun JS. Application of ensemble artificial neural network for the classification of white blood cells using microscopic blood images. International Journal of Computational Systems Engineering. 2018;4:202. (PMID: 10.1504/IJCSYSE.2018.091407)
Sahlol AT, Kollmannsberger P, Ewees AA. Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Scientific Reports. 2020;10.
Yao J, Huang X, Wei M, Han W, Xu X, Wang R, et al. High-efficiency classification of white blood cells based on object detection. Journal of Healthcare Engineering. 2021;2021:1–11.
Singh I, Singh NP, Singh HP, Bawankar S, Ngom A. Blood cell types classification using cnn. Bioinformatics and Biomedical Engineering. 2020;p. 727–738.
Khan S, Sajjad M, Hussain T, Ullah A, Imran AS. A review on traditional machine learning and deep learning models for WBCs classification in blood smear images. Ieee Access. 2020;9:10657–10673. (PMID: 10.1109/ACCESS.2020.3048172)
Deshpande NM, Gite S, Aluvalu R. A review of microscopic analysis of blood cells for disease detection with AI perspective. PeerJ Computer Science. 2021;7:e460. (PMID: 33981834808042710.7717/peerj-cs.460)
Ozdemir C, Dogan Y, Kaya Y. RGB-Angle-Wheel: A new data augmentation method for deep learning models. Knowledge-Based Systems. 2024;291:111615. (PMID: 10.1016/j.knosys.2024.111615)
Toğaçar M, Ergen B, Sertkaya ME. Subclass separation of white blood cell images using convolutional neural network models. Elektronika Ir Elektrotechnika. 2019;25:63–68. (PMID: 10.5755/j01.eie.25.5.24358)
Wang Q, Wang J, Zhou M, Li Q, Wen Y, Chu J. A 3D attention networks for classification of white blood cells from microscopy hyperspectral images. Optics & Laser Technology. 2021;139:106931. (PMID: 10.1016/j.optlastec.2021.106931)
Basnet J, Alsadoon A, Prasad P, Aloussi SA, Alsadoon OH. A novel solution of using deep learning for white blood cells classification: Enhanced loss function with regularization and weighted loss (ELFRWL). Neural Processing Letters. 2020;52:1517–1553. (PMID: 10.1007/s11063-020-10321-9)
Jiang M, Liu C, Qin F, Du L, Zhang M. White blood cells classification with deep convolutional neural networks. International Journal of Pattern Recognition and Artificial Intelligence. 2018;32:1857006. (PMID: 10.1142/S0218001418570069)
Yao X, Sun K, Bu X, Zhao C, Jin Y. Classification of white blood cells using weighted optimized deformable convolutional neural networks. Artificial Cells, Nanomedicine, and Biotechnology. 2021;49(1):147–155. (PMID: 3353365610.1080/21691401.2021.1879823)
Khan A, Eker A, Chefranov A, Demirel H. White blood cell type identification using multi-layer convolutional features with an extreme-learning machine. Biomedical Signal Processing and Control. 2021;69:102932. (PMID: 10.1016/j.bspc.2021.102932)
Çınar A, Tuncer SA. Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM. SN Applied Sciences. 2021;3:1–11. (PMID: 10.1007/s42452-021-04485-9)
Özyurt F. A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine. Soft Computing. 2020;24(11):8163–8172. (PMID: 10.1007/s00500-019-04383-8)
Patil A, Patil M, Birajdar G. White blood cells image classification using deep learning with canonical correlation analysis. Irbm. 2021;42(5):378–389. (PMID: 10.1016/j.irbm.2020.08.005)
Baghel N, Verma U, Nagwanshi KK. WBCs-Net: Type identification of white blood cells using convolutional neural network. Multimedia Tools and Applications. 2022;81(29):42131–42147. (PMID: 10.1007/s11042-021-11449-z)
Erten M, Barua PD, Dogan S, Tuncer T, Tan RS, Acharya U. ConcatNeXt: An automated blood cell classification with a new deep convolutional neural network. Multimedia Tools and Applications. 2024;p. 1–19.
Razzak MI, Naz S. Microscopic Blood Smear Segmentation and Classification Using Deep Contour Aware CNN and Extreme Machine Learning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2017. p. 801–807.
Yu W, Chang J, Yang C, Zhang L, Shen H, Xia Y, et al. Automatic classification of leukocytes using deep neural network. 2017 IEEE 12th International Conference on ASIC (ASICON). 2017;p. 1041–1044. Available from: https://api.semanticscholar.org/CorpusID:28326840 .
Liang G, Hong H, Xie W, Zheng L. Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification. IEEE Access. 2018;6:36188–36197. Available from: https://api.semanticscholar.org/CorpusID:49870021 .
Hegde RB, Prasad K, Hebbar H, Singh BMK. Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images. Biocybernetics and Biomedical Engineering. 2019;39:382–392. (PMID: 10.1016/j.bbe.2019.01.005)
El-Seoud SA, Siala MH, McKee G. Detection and classification of white blood cells through deep learning techniques. International Journal of Online and Biomedical Engineering (iJOE). 2020;16:94. (PMID: 10.3991/ijoe.v16i15.15481)
Banik PP, Saha R, Kim K. An automatic nucleus segmentation and cnn model based classification method of white blood cell. Expert Systems With Applications. 2020;149:113211. (PMID: 10.1016/j.eswa.2020.113211)
Basnet J, Alsadoon A, Prasad PWC, Aloussi SA, Alsadoon OH. A novel solution of using deep learning for white blood cells classification: enhanced loss function with regularization and weighted loss (elfrwl). Neural Processing Letters. 2020;52:1517–1553. (PMID: 10.1007/s11063-020-10321-9)
Baydilli YY, Atila Ü. Classification of white blood cells using capsule networks. Computerized Medical Imaging and Graphics. 2020;80:101699. (PMID: 3200008710.1016/j.compmedimag.2020.101699)
Kutlu H, Avci E, Özyurt F. White blood cells detection and classification based on regional convolutional neural networks. Medical hypotheses. 2020;135:109472. (PMID: 3176024810.1016/j.mehy.2019.109472)
Cheuque C, Querales M, León R, Salas R, Torres R. An efficient multi-level convolutional neural network approach for white blood cells classification. Diagnostics. 2022;12(2):248. (PMID: 35204339887131910.3390/diagnostics12020248)
Girdhar A, Kapur H, Kumar V. Classification of white blood cell using convolution neural network. Biomedical Signal Processing and Control. 2022;71:103156. (PMID: 10.1016/j.bspc.2021.103156)
Chen S, Tan X, Wang B, Lu H, Hu X, Fu Y. Reverse attention-based residual network for salient object detection. IEEE Transactions on Image Processing. 2020;29:3763–3776. (PMID: 10.1109/TIP.2020.2965989)
Landeros C, Oh J, Weissleder R, Lee H. Deep learning pipeline for automated cell profiling from cyclic imaging. Scientific Reports. 2024;14(1):23600. (PMID: 393849071146478910.1038/s41598-024-74597-w)
Cakir S, Kahraman DC, Cetin-Atalay R, Cetin AE. Contrast enhancement of microscopy images using image phase information. IEEE Access. 2018;6:3839–3850. (PMID: 10.1109/ACCESS.2018.2796646)
Heo YJ, Lee D, Kang J, Lee K, Chung WK. Real-time image processing for microscopy-based label-free imaging flow cytometry in a microfluidic chip. Scientific reports. 2017;7(1):11651. (PMID: 28912565559953510.1038/s41598-017-11534-0)
Schapiro D, Sokolov A, Yapp C, Chen YA, Muhlich JL, Hess J, et al. MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nature methods. 2022;19(3):311–315. (PMID: 3482447710.1038/s41592-021-01308-y)
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. ConvNeXt: A ConvNet for the 2020s. arXiv preprint arXiv:2201.03545 . 2022;.
Erdogan A. ConvNeXt - Next Generation of Convolutional Networks. Medium; 2023. Accessed: 2025-01-01. Available from: https://medium.com/@atakanerdogan305/convnext-next-generation-of-convolutional-networks-325607a08c46 .
Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 2020. p. 11534–11542.
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV); 2021. p. 10012–10022.
Tsang SH. Review: Swin Transformer. Medium; 2021. Accessed: 2025-01-01. Available from: https://sh-tsang.medium.com/review-swin-transformer-3438ea335585 .
Research M. Swin Transformer. GitHub; 2021. https://github.com/microsoft/Swin-Transformer .
Dai Y, Gieseke F, Oehmcke S, Wu Y, Barnard K. Attentional Feature Fusion. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision; 2021. p. 3560–3569.
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the Inception Architecture for Computer Vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2015. p. 2818–2826.
Boldú L, Merino A, Acevedo A, Molina A, Rodellar J. A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images. Computer Methods and Programs in Biomedicine. 2021;202:105999. (PMID: 3361814510.1016/j.cmpb.2021.105999)
Baby D, Devaraj SJ, et al. Leukocyte classification based on transfer learning of VGG16 features by K-nearest neighbor classifier. In: 2021 3rd International Conference on Signal Processing and Communication (ICPSC). IEEE; 2021. p. 252–256.
Sen B, Ganesh A, Bhan A, Dixit S. Deep Learning based diagnosis of sickle cell anemia in human RBC. In: 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM). IEEE; 2021. p. 526–529.
Sheng B, Zhou M, Hu M, Li Q, Sun L, Wen Y. A blood cell dataset for lymphoma classification using faster R-CNN. Biotechnology & Biotechnological Equipment. 2020;34(1):413–420. (PMID: 10.1080/13102818.2020.1765871)
Acevedo A, Merino A, Alférez S, Molina Á, Boldú L, Rodellar J. A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data in brief. 2020;30.
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Contributed Indexing:
Keywords: Blood cell classification; ConvNeXt; Feature fusion; Medical image analysis; Swin Transformer
Entry Date(s):
Date Created: 20250429 Date Completed: 20260219 Latest Revision: 20260222
Update Code:
20260222
PubMed Central ID:
PMC12920894
DOI:
10.1007/s10278-025-01479-6
PMID:
40301289
Database:
MEDLINE
*Further Information*
*In the rapidly evolving field of medical image analysis, the precise classification of blood cells plays a crucial role in diagnosing and monitoring numerous hematological disorders. Traditional methods, while effective, often require significant manual effort and expert knowledge, leading to potential delays and inconsistencies in diagnosis. Addressing these challenges, this paper introduces a groundbreaking dual-path deep learning architecture that synergistically combines ConvNeXt and Swin Transformer networks. This innovative approach leverages the strengths of convolutional neural networks for local feature extraction and transformers for global context integration, effectively capturing the complex morphological variations in blood cells. Furthermore, the incorporation of a Multi-scale Preprocessing Module (MPM) significantly enhances the image quality, employing techniques such as local contrast enhancement, global illumination normalization, and morphological feature enhancement to improve the visibility and differentiation of cellular features. Tested on a comprehensive dataset of 17,092 blood cell images, our model achieves an unprecedented accuracy of 99.98%, demonstrating superior performance over existing methods. This level of accuracy not only underscores the effectiveness of our model but also highlights its potential to serve as a reliable tool in clinical settings, facilitating faster and more accurate blood cell analysis. By automating the classification process with high precision, our approach promises to enhance diagnostic workflows, reduce the workload on medical professionals, and ultimately contribute to better patient outcomes in the field of hematology.
(© 2025. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)*
*Declarations. Ethical Approval: This article does not contain any studies with human participants or animals performed by any of the authors. Consent to Participate: Not applicable Consent for Publication: The author affirms that this article contains no personal data of any individual person. Conflict of Interest: The author declares no competing interests.*