A Robust Deep Learning Model for WBC Classification using Capsule Net and Stacked Sparse Auto Encoder coupled with Mayfly Optimization
Keywords:
WBC, Bilateral filter, CLAHE, Capsule Net, Auto encoder, Mayfly optimizationAbstract
White Blood Cells build the base of the human immunity and hence hold a critical place in haematological disease diagnosis. Since there exist 12 distinct types in white blood cells which vary by only a small margin, propose a hybrid classification model which combines deep learning techniques along with optimization algorithms for achieving higher performance. The proposed model utilizes high-performance, state-of-the-art technologies. A total of 1460 images are obtained from the standard Kaggle database. The input images are preprocessed using bilateral filter and contrast limited adaptive histogram equalization algorithm is applied for contrast enhancement. The preprocessed imaged are then segmented using UNet architecture of convolutional neural networks. Features are then extracted using Capsule Net machine learning approach. Finally, WBC images are classified into five types namely Eosinophils, Neutrophils, Basophils, Lymphocytes and Monocytes using stacked sparse auto encoder and optimized with Mayfly optimization algorithm. The proposed model is compared with existing algorithms like Support Vector Machine, DenseNet, Inceptionv3, ResNet, Convolutional Neural Network and is found to have superior performance. It achieves an accuracy of 97.79%, precision score of 97.40%, Recall of 97.40%, specificity of 97.17%, F1-Score of 97.4% and ROC value of 0.998.
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B.S.S. Rao, and B.S. Rao, 2023. An Effective WBC Segmentation and Classification Using MobilenetV3–ShufflenetV2 Based Deep Learning Framework. IEEE Access, 11, pp.27739-27748.
P.V. Arun, K.M. Buddhiraju, and A. Porwal, 2019. Capsulenet-based spatial–spectral classifier for hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(6), pp.1849-1865.
L. Cai, J. Gao, and D. Zhao, 2020. A review of the application of deep learning in medical image classification and segmentation. Annals of translational medicine, 8(11).
A. Shahzad, M. Raza, J.H. Shah, M. Sharif, and R.S. Nayak, 2022. Categorizing white blood cells by utilizing deep features of proposed 4B-AdditionNet-based CNN network with ant colony optimization. Complex & Intelligent Systems, pp.1-17.
A. Meenakshi, J.A. Ruth, V.R. Kanagavalli, and R. Uma, 2022. Automatic classification of white blood cells using deep features based convolutional neural network. Multimedia Tools and Applications, 81(21), pp.30121-30142.
S.H. Kassani, P.H. Kassani, M.J. Wesolowski, Schneider, K.A. and Deters, R., 2019, October. A hybrid deep learning architecture for leukemic B-lymphoblast classification. In 2019 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 271-276). IEEE.
A.H. Alharbi, C.V. Aravinda, M. Lin, P.S. Venugopala, P. Reddicherla, and M.A. Shah, 2022. Segmentation and classification of white blood cells using the UNet. Contrast Media & Molecular Imaging, 2022.
S. Alagu, A.P. N, and B.B. K, 2021. Automatic detection of acute lymphoblastic leukemia using UNET based segmentation and statistical analysis of fused deep features. Applied Artificial Intelligence, 35(15), pp.1952-1969.
D. Yentrapragada, 2023. Deep features based convolutional neural network to detect and automatic classification of white blood cells. Journal of Ambient Intelligence and Humanized Computing, 14(7), pp.9191-9205.
M.A. Munir, M.A. Aslam, M. Shafique, R. Ahmed, and Z. Mehmood, 2022. Deep stacked sparse autoencoders-a breast cancer classifier. Mehran University Research Journal Of Engineering & Technology, 41(1), pp.41-52.
M. Jiang, M. Shao, X. Yang, L. He, T. Peng, T. Wang, Z. Ke, Z. Wang, S. Fang, Y. Mao, and X. Ouyang, 2022. Automatic classification of red blood cell morphology based on quantitative phase imaging. International Journal of Optics, 2022.
M. Kim, J. Yun, Y. Cho, K. Shin, R. Jang, H.J. Bae, and N. Kim, 2019. Deep learning in medical imaging. Neurospine, 16(4), p.657.
M. Elhoseny, and K. Shankar, 2019. Optimal bilateral filter and convolutional neural network based denoising method of medical image measurements. Measurement, 143, pp.125-135.
P. Deep, S. Shukla, E. Pandey, and A. Sinha, 2020. Detection of Abnormalities in Blood Sample Using WBC Differential Count. In Soft Computing for Problem Solving 2019: Proceedings of SocProS 2019, Volume 1 (pp. 227-240). Springer Singapore.
S. Kadry, V. Rajinikanth, D.Taniar, R. Damaševičius, and X.P.B. Valencia, 2022. Automated segmentation of leukocyte from hematological images—a study using various CNN schemes. The Journal of Supercomputing, pp.1-21.
H. Liu, Z. Jiao, W. Han, and B. Jing, 2021. Identifying the histologic subtypes of non-small cell lung cancer with computed tomography imaging: A comparative study of capsule net, convolutional neural network, and radiomics. Quantitative Imaging in Medicine and Surgery, 11(6), p.2756.
I.D. Mienye, and Y. Sun, 2021. Improved heart disease prediction using particle swarm optimization based stacked sparse autoencoder. Electronics, 10(19), p.2347.
A. Khamparia, G. Saini, B. Pandey, S. Tiwari, D. Gupta, and A. Khanna, 2020. KDSAE: Chronic kidney disease classification with multimedia data learning using deep stacked autoencoder network. Multimedia Tools and Applications, 79, pp.35425-35440.
V.J. Kadam, S.M. Jadhav, and K. Vijayakumar, 2019. Breast cancer diagnosis using feature ensemble learning based on stacked sparse autoencoders and softmax regression. Journal of medical systems, 43(8), p.263.
K. Zervoudakis, and S. Tsafarakis, 2020. A mayfly optimization algorithm. Computers & Industrial Engineering, 145, p.106559.
N. Veeraiah, Y. Alotaibi, and A.F. Subahi, 2023. MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images. Computer Systems Science and Engineering, 46(2), pp.2039-2058.
L. Ma, R. Shuai, X. Ran, W. Liu, and C.Ye, 2020. Combining DC-GAN with ResNet for blood cell image classification. Medical & biological engineering & computing, 58, pp.1251-1264.
E. H Mohamed, W. H El-Behaidy, G. Khoriba, and J. Li, 2020. Improved white blood cells classification based on pre-trained deep learning models. Journal of Communications Software and Systems, 16(1), pp.37-45.
Z. Wang, J. Xiao, J. Li, H. Li, and L. Wang, 2022. WBC-AMNet: Automatic classification of WBC images using deep feature fusion network based on focalized attention mechanism. Plos one, 17(1), p.e0261848.
S. Ramaneswaran, K. Srinivasan, P.D.R. Vincent, and C.Y. Chang, 2021. Hybrid inception v3 XGBoost model for acute lymphoblastic leukemia classification. Computational and Mathematical Methods in Medicine, 2021, pp.1-10.
Juan Garcia, Guðmundsdóttir Anna, Johansson Anna, Maria Jansen, Anna Wagner. Machine Learning for Predictive Maintenance and Decision Making in Manufacturing. Kuwait Journal of Machine Learning, 2(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/212
Sherje, N. P., Agrawal, S. A., Umbarkar, A. M., Dharme, A. M., & Dhabliya, D. (2021). Experimental evaluation of mechatronics based cushioning performance in hydraulic cylinder. Materials Today: Proceedings, doi:10.1016/j.matpr.2020.12.1021
Christopher Thomas, Stephen Wright, Juan Martinez, Alejandro Perez, Luis Rodriguez. Integrating Machine Learning and Decision Science for Intelligent Automation. Kuwait Journal of Machine Learning, 2(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/213
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