Abstract
Leukemia is a prominent hematologic malignancy that causes mortality and complications at different ages. In this paper, to provide a more accurate diagnosis of Acute Lymphoblastic Leukemia (ALL), a three-stage model based on transfer learning called variable-kernel channel-spatial attention (VKCS) is proposed. First, a deep pre-trained network extracts high-level features from the blood smear images. In the second stage, two attention mechanisms of variable-kernel spatial attention and variable-kernel channel attention consider spatial and channel information in parallel to improve model performance. The last step is the classification module. The experiments are repeated for different pre-trained networks, as well as for images in RGB, HSV, L*a*b*, and YCbCr color spaces, and by applying morphological operators (erosion and dilation) to images in different color spaces. The best model efficiency accuracy was achieved for images in HSV color space and the use of EfficientNet-V2M for feature extraction. The VKCS model accuracy is 100% for the ALL-IDB1 dataset and 99.6% for the ALL-IDB2 dataset, which are promising results.
Similar content being viewed by others
Data availability
The datasets analyzed during the current study are available from the corresponding author on reasonable request.
References
Abou El-Seoud S, Siala MH, McKee G (2020) Detection and classification of white blood cells through deep learning techniques. Int J Online Biomed Eng (iJOE) 16(15):94–105. https://doi.org/10.3991/ijoe.v16i15.15481
Ahmed N., A. Yigit, Z. Isik, and A. Alpkocak (2019) Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network. (in eng), Diagn (Basel). 9 (3): 104. https://doi.org/10.3390/diagnostics9030104.
Alagu S (2021) Automatic detection of acute lymphoblastic leukemia using UNET based segmentation and statistical analysis of fused deep features. Appl Artif Intell 35(15):1952–1969. https://doi.org/10.1080/08839514.2021.1995974
Ali A, Zhu Y, Zakarya M (2021) A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing. Multimed Tools Appl 80(20):31401–31433. https://doi.org/10.1007/s11042-020-10486-4
Ali A, Zhu Y, Zakarya M (2021) Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks. Inf Sci 577:852–870. https://doi.org/10.1016/j.ins.2021.08.042
Anilkumar KK, Manoj VJ, Sagi TM (2021) Automated detection of leukemia by pretrained deep neural networks and transfer learning: a comparison. Med Eng Phys 98:8–19. https://doi.org/10.1016/j.medengphy.2021.10.006
Anilkumar KK, Manoj VJ, Sagi TM (2021) Automated detection of B cell and T cell acute lymphoblastic Leukaemia using deep learning. IRBM. 43:405–413. https://doi.org/10.1016/j.irbm.2021.05.005
Bodzas A, Kodytek P, Zidek J (2020) Automated detection of acute lymphoblastic leukemia from microscopic images based on human visual perception. (in eng). Front Bioeng Biotechnol 8:1005–1005. https://doi.org/10.3389/fbioe.2020.01005
Boumaraf S, Liu X, Zheng Z, Ma X, Ferkous C (2021) A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images. Biomed Signal Process Control 63:102192. https://doi.org/10.1016/j.bspc.2020.102192
Cha S-M, Lee S-S, Ko B (2021) Attention-Based Transfer Learning for Efficient Pneumonia Detection in Chest X-ray Images. Appl Sci 11(3):1242 [Online]. Available: https://www.mdpi.com/2076-3417/11/3/1242
Das PK, Meher S (2021) An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukemia. Exp Syst Appl 183:115311. https://doi.org/10.1016/j.eswa.2021.115311
Das PK, Nayak B, Meher S (2022) A lightweight deep learning system for automatic detection of blood cancer. Measurement. 191:110762. https://doi.org/10.1016/j.measurement.2022.110762
El Houby EMF (2021) Using transfer learning for diabetic retinopathy stage classification. Applied computing and informatics. Ahead-of-print (ahead-of-print). https://doi.org/10.1108/ACI-07-2021-0191.
He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778. https://doi.org/10.1109/CVPR.2016.90.
Hu J, Shen L, Sun G (2018) Squeeze-and-Excitation Networks. in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recogn 7132–7141. https://doi.org/10.1109/CVPR.2018.00745.
Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely Connected Convolutional Networks. in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2261–2269. https://doi.org/10.1109/CVPR.2017.243.
Jha KK, Dutta HS (2019) Mutual information based hybrid model and deep learning for acute lymphocytic leukemia detection in single cell blood smear images. Comput Methods Prog Biomed 179:104987. https://doi.org/10.1016/j.cmpb.2019.104987
Karthik R, Vaichole TS, Kulkarni SK, Yadav O, Khan F (2022) Eff2Net: an efficient channel attention-based convolutional neural network for skin disease classification. Biomed Signal Process Control 73:103406. https://doi.org/10.1016/j.bspc.2021.103406
Kumar A, Rawat J, Kumar I, Rashid M, Singh KU, al-Otaibi YD, Tariq U (2022) Computer-aided deep learning model for identification of lymphoblast cell using microscopic leukocyte images. Expert Syst 39(4):e12894. https://doi.org/10.1111/exsy.12894
Labati RD, Piuri V, Scotti F (2011) All-IDB: The acute lymphoblastic leukemia image database for image processing. in 2011 18th IEEE International Conference on Image Processing. 2045-2048. https://doi.org/10.1109/ICIP.2011.6115881.
Mishra S, Majhi B, Sa PK (2019) Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection. Biomed Signal Process Control 47:303–311. https://doi.org/10.1016/j.bspc.2018.08.012
Mohammed ZF, Abdulla AA (2021) An efficient CAD system for ALL cell identification from microscopic blood images. Multimed Tools Appl 80(4):6355–6368. https://doi.org/10.1007/s11042-020-10066-6
Pandiyan V, Drissi-Daoudi R, Shevchik S, Masinelli G, le-Quang T, Logé R, Wasmer K (2022) Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process. J Mater Process Technol 303:117531. https://doi.org/10.1016/j.jmatprotec.2022.117531
Qin F-W, Gao N, Peng Y, Wu Z, Shen S, Grudtsin A (2018) Fine-grained leukocyte classification with deep residual learning for microscopic images. Comput Methods Prog Biomed 162:243–252
Rastogi P, Khanna K, Singh V (2022) LeuFeatx: Deep learning–based feature extractor for the diagnosis of acute leukemia from microscopic images of peripheral blood smear. Comput Biol Med 142:105236. https://doi.org/10.1016/j.compbiomed.2022.105236
Rawat J, Virmani J, Singh A, Bhadauria HS, Kumar I, Devgan JS (2020) FAB classification of acute leukemia using an ensemble of neural networks. Evol Intel 15:99–117. https://doi.org/10.1007/s12065-020-00491-9
Rodrigues LF, Backes AR, Travençolo BAN, de Oliveira GMB (2022) Optimizing a deep residual neural network with genetic algorithm for acute lymphoblastic leukemia classification. J Digit Imaging 35:623–637. https://doi.org/10.1007/s10278-022-00600-3
Rosales-Pérez A et al (2022) Chapter 20 - a review on machine learning techniques for acute leukemia classification. In: Torres-García AA, Reyes-García CA, Villaseñor-Pineda L, Mendoza-Montoya O (eds) Biosignal Processing and Classification Using Computational Learning and Intelligence. Academic Press, pp 429–446
Sahlol AT, Kollmannsberger P, Ewees AA (2020) Efficient classification of white blood cell leukemia with improved swarm optimization of deep features. Sci Rep 10(1):2536. https://doi.org/10.1038/s41598-020-59215-9
Sai AL, Omar E-G (2021) Human Activity Recognition: A Comparison of Machine Learning Approaches. J Midwest Assoc Inf Syst (JMWAIS). 2021 (1). https://doi.org/10.17705/3jmwa.000065
Sandler M, Howard AG, Zhu M, Zhmoginov A, Chen L-C (2018) MobileNetV2: inverted residuals and linear bottlenecks. Presented at the Conference on Computer Vision and Pattern Recognition, Salt Lake City. [Online]. https://doi.org/10.1109/CVPR.2018.00474
Shafique S, Tehsin S (2018) Acute lymphoblastic leukemia detection and classification of its subtypes using Pretrained deep convolutional neural networks. Technol Cancer Res Treat 17:1533033818802789. https://doi.org/10.1177/1533033818802789
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. Presented at the 3rd International Conference on Learning Representations, San Diego. [Online]. Available: http://arxiv.org/abs/1409.1556
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global Cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the Inception Architecture for Computer Vision. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2818–2826. https://doi.org/10.1109/CVPR.2016.308.
Tan M, Le Q (2021) EfficientNetV2: smaller models and faster training. Presented at the Proceedings of the 38th International Conference on Machine Learning, Proceedings of Machine Learning Research. [Online]. Available: https://proceedings.mlr.press/v139/tan21a.html. Accessed 18-24 Jul 2021
Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 11531–11539. https://doi.org/10.1109/CVPR42600.2020.01155
Wang A, Wang M, Wu H, Jiang K, Iwahori Y (2020) A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet. Sensors 20(4):1151 [Online]. Available: https://www.mdpi.com/1424-8220/20/4/1151
Wang Z, Xiao J, Li J, Li H, Wang L (2022) WBC-AMNet: automatic classification of WBC images using deep feature fusion network based on focalized attention mechanism. PLoS One 17(1):e0261848. https://doi.org/10.1371/journal.pone.0261848
Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. Springer Int Publishing Comput Vis ECCV 2018:3–19
Xu C, Lou M, Qi Y, Wang Y, Pi J, Ma Y (2021) Multi-scale attention-guided network for mammograms classification. Biomed Signal Process Control 68:102730. https://doi.org/10.1016/j.bspc.2021.102730
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author has no conflicts of interest to disclose.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Masoudi, B. VKCS: a pre-trained deep network with attention mechanism to diagnose acute lymphoblastic leukemia. Multimed Tools Appl 82, 18967–18983 (2023). https://doi.org/10.1007/s11042-022-14212-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-14212-0