Abstract
Leukemia is a life-threatening condition affecting people globally, making accurate diagnosis crucial for timely intervention. Consequently, researchers have been exploring automated methods to enable prompt action. The classification of leukemia into multiple subtypes according to WHO standards presents a unique challenge. Unlike binary classification, interclass features are highly similar, leading to misclassification. Ergo, we employ attention mechanisms to tackle this problem. Our proposed deep learning architecture combines transfer learning with attention mechanisms to classify subtypes of leukemia accurately. Using a publicly available dataset of blood cell images that adhered to WHO standards, we illustrate the potency of our approach. Our DenseNet201 with CBAM model achieves a remarkable 99.85% overall accuracy without resorting to data augmentation, surpassing previous methods on this dataset and attaining state-of-the-art results compared to other leukemia literature. To interpret the model’s decision-making process and evaluate the efficacy of the attention mechanism in identifying discriminating features, we showcase GradCAM images and intermediate layer feature maps generated from our custom CNN. The proposed approach enhances leukemia classification accuracy and efficiency, providing clinical decision-making insights.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zakir Ullah M, Zheng Y, Song J, Aslam S, Xu C, Kiazolu GD, Wang L (2021) An attention-based convolutional neural network for acute lymphoblastic leukemia classification. Appl Sci 11(22):10662
Society AC: American cancer society: cancer facts & statistics. https://cancerstatisticscenter.cancer.org/#!/cancer-site/Leukemia. Accessed on 7 Apr 2023
Das PK, Diya V, Meher S, Panda R, Abraham A (2022) A systematic review on recent advancements in deep and machine learning based detection and classification of acute lymphoblastic leukemia. IEEE Access
Sajon TI, Chowdhury M, Srizon AY, Faruk MF, Hasan SM, Sayeed A, Rahman AM (2023) Recognition of leukemia sub-types using transfer learning and extraction of distinguishable features using an effective machine learning approach. In: 2023 International conference on electrical, computer and communication engineering (ECCE). IEEE, pp 1–6
Woo S, Park J, Lee JY, Kweon IS (2018) Cbam: convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV), pp 3–19
Pałczyński K, Śmigiel S, Gackowska M, Ledziński D, Bujnowski S, Lutowski Z (2021) IoT application of transfer learning in hybrid artificial intelligence systems for acute lymphoblastic leukemia classification. Sensors 21(23):8025
Ghaderzadeh M, Aria M, Hosseini A, Asadi F, Bashash D, Abolghasemi H (2022) A fast and efficient CNN model for b-all diagnosis and its subtypes classification using peripheral blood smear images. Int J Intell Syst 37(8):5113–5133
Anilkumar K, Manoj V, Sagi T (2022) Automated detection of b cell and t cell acute lymphoblastic leukaemia using deep learning. Irbm 43(5):405–413
Saeed A, Shoukat S, Shehzad K, Ahmad I, Eshmawi A, Amin AH, Tag-Eldin E (2022) A deep learning-based approach for the diagnosis of acute lymphoblastic leukemia. Electronics 11(19):3168
Sampathila N, Chadaga K, Goswami N, Chadaga RP, Pandya M, Prabhu S, Bairy MG, Katta SS, Bhat D, Upadya SP (2022) Customized deep learning classifier for detection of acute lymphoblastic leukemia using blood smear images. In: Healthcare, vol 10. MDPI, p 1812
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR, pp 6105–6114
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30
Zhang H, Goodfellow I, Metaxas D, Odena A (2019) Self-attention generative adversarial networks. In: International conference on machine learning. PMLR, pp 7354–7363
Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807–814
Ackley DH, Hinton GE, Sejnowski TJ (1985) A learning algorithm for Boltzmann machines. Cogn Sci 9(1):147–169
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. ar**v preprint ar**v:1412.6980
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sajon, T.I. et al. (2024). Attention Mechanism-Enhanced Deep CNN Architecture for Precise Multi-class Leukemia Classification. In: Arefin, M.S., Kaiser, M.S., Bhuiyan, T., Dey, N., Mahmud, M. (eds) Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning. BIM 2023. Lecture Notes in Networks and Systems, vol 867. Springer, Singapore. https://doi.org/10.1007/978-981-99-8937-9_24
Download citation
DOI: https://doi.org/10.1007/978-981-99-8937-9_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8936-2
Online ISBN: 978-981-99-8937-9
eBook Packages: EngineeringEngineering (R0)