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Abstract: Blood cell (BC) classification holds significant importance in medical diagnostics as it enables the identification and differentiation of various types of BCs, which is crucial for detecting specific infections, disorders, or conditions, and guiding appropriate treatment decisions. Accurate BC classification simplifies the evaluation of immune system performance and the diagnosis of various ailments such as infections, leukemia, and other hematological disorders. Deep learning algorithms perform excellently in the automated identification and differentiation of various types of BCs. One of the advanced deep learning models, EfficientNet has shown remarkable performance with limited datasets, another model Swin Transformer’s capability to capture intricate patterns and features makes it more accurate, albeit with limitations due to its large number of parameters. However, medical image datasets are often limited, necessitating a solution that balances accuracy and efficiency. To address this, we propose a novel hybrid model, EfficientSwin, which combines Swin Transformer’s and EfficientNet’s strengths. We first fine-tuned the Swin Transformer on a blood cell dataset comprising wihite blood cells, red blood cells and platelets, achieving promising outcomes. Subsequently, our hybrid model, EfficientSwin, outperformed the standalone Swin Transformer, achieving an impressive 98.14% accuracy in blood cell classification. Furthermore, we compared our approach with previous research on white blood cell datasets, showcasing the superiority of EfficientSwin in accurately classifying blood cells. Additionally, we utilized saliency maps to visually represent the classification results.
- Install timm to load pretrained models
pip install timm- Install dataset bloodMNIST from medMNIST
pip install medmnist- Check available dataset
python -m medmnist availableDownload the pretrained weights and run the following command for evaluation and training of proposed hybrid model on bloodMNIST dataset. Make directory "models" in your current directory to save model.
python EfficientSwin_proposed.py --batch_size 32 --epochs 35 --lr 0.001 --save_fig training_validation_accuracy.png
- [14] Jung, Changhun, et al. "WBC image classification and generative models based on convolutional neural network." BMC Medical Imaging 22.1 (2022): 1-16.
- [15] Acevedo, Andrea, et al. "Recognition of peripheral blood cell images using convolutional neural networks." Computer methods and programs in biomedicine 180 (2019): 105020.
- [16] Qin, Feiwei, et al. "Fine-grained leukocyte classification with deep residual learning for microscopic images." Computer methods and programs in biomedicine 162 (2018): 243-252.
- [17] Sharma, Mayank, Aishwarya Bhave, and Rekh Ram Janghel. "White blood cell classification using convolutional neural network." Soft Computing and Signal Processing: Proceedings of ICSCSP 2018, Volume 1. Springer Singapore, 2019.
- [18] Zhao, Jianwei, et al. "Automatic detection and classification of leukocytes using convolutional neural networks." Medical & biological engineering & computing 55 (2017): 1287-1301.
- [19] Ma, Li, et al. "Combining DC-GAN with ResNet for blood cell image classification." Medical & biological engineering & computing 58 (2020): 1251-1264.
- [20] Şengür, Abdulkadir, et al. "White blood cell classification based on shape and deep features." 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). Ieee, 2019.
- [21] Yang, Jiancheng, et al. "MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification." Scientific Data 10.1 (2023): 41.