Attention Mechanism-Enhanced Deep CNN Architecture for Precise Multi-class Leukemia Classification

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.