Abstract
Proper classification of white blood cells (WBCs) remains a critical task for medical diagnosis in leukemia, infections, and hematological disorders. WBC classification is based mainly on Convolutional Neural Networks (CNNs) as the main methodology, despite the challenges in such methods in scanning microscopic images for distant dependencies. The current study adopts a hybrid model that combines Vision Transformers (ViTs) with CNNs for better WBC classification. The model has a ViT component that uses self-attention mechanisms to obtain whole-image features through global features extraction, while at the same time, neighborhood information is being extracted by the CNN module. The introduced model is evaluated using theBCCD Dataset (Blood Cell Count and Detection) for binary and multi-class separation of granulocytes/agranulocytes. Experimental tests indicate that the combined approach has robust classification accuracy with guaranteed reliable results in various evaluation metrics. The suggested method achieved 99.20% training accuracy, 87.90% test accuracy, a precision of 0.7083, a recall of 0.7000, and an F1-score of 0.6970, affirming its excellent classification precision. This hybrid deep learning model shows enhanced performance and interpretability, supporting its application in clinical diagnostic processes. These findings attest to excellent classification performance, positioning the model as an excellent choice for automated hematological diagnosis. The study opens up new horizons for combining CNNs and ViTs for improved medical image analysis while setting future development goals in WBC classification. Deep learning hybrid models have shown their significance for clinical diagnostics in the study through the development of enhanced detection systems against hematological diseases.
References
- Asghar, Rabia, Sanjay Kumar, Arslan Shaukat, and Paul Hynds. "Classification of white blood cells (leucocytes) from blood smear imagery using machine and deep learning models: A global scoping review." Plos one 19, no. 6 (2024): e0292026.
- Almurayziq, Tariq S., Ebrahim Mohammed Senan, Badiea Abdulkarem Mohammed, Zeyad Ghaleb Al-Mekhlafi, Gharbi Alshammari, Abdullah Alshammari, Mansoor Alturki, and Abdullah Albaker. "Deep and hybrid learning techniques for diagnosing microscopic blood samples for early detection of white blood cell diseases." Electronics 12, no. 8 (2023): 1853.
- Rao, Bairaboina Sai Sambasiva, and Battula Srinivasa Rao. "An effective WBC segmentation and classification using MobilenetV3–ShufflenetV2 based deep learning framework." IEEE Access 11 (2023): 27739-27748.
- Yentrapragada, Divyateja. "Deep features based convolutional neural network to detect and automatic classification of white blood cells." Journal of Ambient Intelligence and Humanized Computing 14, no. 7 (2023): 9191-9205.
- Elhassan, Tusneem A., Mohd Shafry Mohd Rahim, Mohd Hashim Siti Zaiton, Tan Tian Swee, Taqwa Ahmed Alhaj, Abdulalem Ali, and Mahmoud Aljurf. "Classification of atypical white blood cells in acute myeloid leukemia using a two-stage hybrid model based on deep convolutional autoencoder and deep convolutional neural network." Diagnostics 13, no. 2 (2023): 196.
- Olayah, Fekry, Ebrahim Mohammed Senan, Ibrahim Abdulrab Ahmed, and Bakri Awaji. "Blood slide image analysis to classify WBC types for prediction haematology based on a hybrid model of CNN and handcrafted features." Diagnostics 13, no. 11 (2023): 1899.
- Cheuque, César, Marvin Querales, Roberto León, Rodrigo Salas, and Romina Torres. "An efficient multi-level convolutional neural network approach for white blood cells classification." Diagnostics 12, no. 2 (2022): 248.
- Jung, Changhun, Mohammed Abuhamad, David Mohaisen, Kyungja Han, and DaeHun Nyang. "WBC image classification and generative models based on convolutional neural network." BMC Medical Imaging 22, no. 1 (2022): 94.
- Davamani, K. Anita, CR Rene Robin, D. Doreen Robin, and L. Jani Anbarasi. "Adaptive blood cell segmentation and hybrid Learning-based blood cell classification: A Meta-heuristic-based model." Biomedical Signal Processing and Control 75 (2022): 103570.
- Balasubramanian, Kishore, N. P. Ananthamoorthy, and K. Ramya. "An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm." Neural Computing and Applications 34, no. 18 (2022): 16089-16101.
- Patil, A. M., M. D. Patil, and G. K. Birajdar. "White blood cells image classification using deep learning with canonical correlation analysis." Irbm 42, no. 5 (2021): 378-389.
- Çınar, Ahmet, and Seda Arslan Tuncer. "Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM." SN Applied Sciences 3 (2021): 1-11.
- Khan, Siraj, Muhammad Sajjad, Tanveer Hussain, Amin Ullah, and Ali Shariq Imran. "A review on traditional machine learning and deep learning models for WBCs classification in blood smear images." Ieee Access 9 (2020): 10657-10673.
- Malkawi, Areej, Rawan Al-Assi, Taimaa Salameh, Bara’ah Sheyab, Hiam Alquran, and Ali Mohammad Alqudah. "White blood cells classification using convolutional neural network hybrid system." In 2020 IEEE 5th middle east and Africa conference on biomedical engineering (MECBME), pp. 1-5. IEEE, 2020.
- Dosovitskiy A, et al. "An image is worth 16x16 words: Transformers for image recognition at scale." arXiv (2020): 2010.11929.
- Özyurt, Fatih. "A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine." Soft Computing 24, no. 11 (2020): 8163-8172.
- Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014)