Mamba-Based Ensemble learning for White Blood Cell Classification
This work addresses the problem of automated WBC classification for medical diagnostics, offering a scalable solution for resource-constrained environments, but it is incremental as it adapts existing Mamba models to a specific domain.
The paper tackles white blood cell classification by introducing a Mamba-based ensemble learning framework, which improves efficiency without compromising accuracy, validated on a new dataset called Chula-WBC-8.
White blood cell (WBC) classification assists in assessing immune health and diagnosing various diseases, yet manual classification is labor-intensive and prone to inconsistencies. Recent advancements in deep learning have shown promise over traditional methods; however, challenges such as data imbalance and the computational demands of modern technologies, such as Transformer-based models which do not scale well with input size, limit their practical application. This paper introduces a novel framework that leverages Mamba models integrated with ensemble learning to improve WBC classification. Mamba models, known for their linear complexity, provide a scalable alternative to Transformer-based approaches, making them suitable for deployment in resource-constrained environments. Additionally, we introduce a new WBC dataset, Chula-WBC-8, for benchmarking. Our approach not only validates the effectiveness of Mamba models in this domain but also demonstrates their potential to significantly enhance classification efficiency without compromising accuracy. The source code can be found at https://github.com/LewisClifton/Mamba-WBC-Classification.