Efficient hybrid CNN-transformer model for accurate blood cancer detection

When the number of white blood cells (WBCs) in the human body becomes imbalanced, leukemia (blood cancer) can develop, affecting individuals of all ages. Early and accurate detection is crucial for improving patient outcomes, yet manual diagnosis is time-consuming and prone to subjectivity. This study proposes an efficient hybrid convolutional neural network (CNN)-transformer model for automated blood cancer detection, integrating convolutional layers for localized feature extraction with transformer-based attention mechanisms for capturing global dependencies. The architecture employs depthwise separable convolutions, efficient multi-head self-attention (EMHSA), and an efficient multilayer perceptron (EMLP) block, optimized via Bayesian hyperparameter tuning. The proposed model was evaluated on two publicly available datasets: the Blood Cancer dataset (binary classification) and the Blood Cells Cancer (ALL) dataset (four-class classification). Using a 50:50 training-testing split, the model achieved 100% accuracy, 100% precision, 100% recall, and 100% F1-score on the Blood Cancer dataset, and 99% accuracy, 99% precision, 100% recall, and 99% F1-score on the ALL dataset. Additional experiments with 80:20, 70:30, and 60:40 splits confirmed consistent performance above 98% across all metrics, demonstrating strong robustness. The model contains only 2.04 million trainable parameters, significantly fewer than standard CNN or transformer-based architectures, making it computationally lightweight and suitable for deployment in resource-constrained clinical environments. These results highlight the potential of the proposed hybrid framework to provide accurate and efficient blood cancer classification, advancing the applicability of deep learning in hematological diagnostics.