Enhancing white blood cell classification via fixed segmentation and learnable blending

Abstract

Accurate and timely classification of white blood cells (WBCs) is crucial for diagnosing a myriad of hematological disorders, including leukemia. While deep learning models, particularly Convolutional Neural Networks (CNNs), have shown promise in automating this task from microscopic blood smear images, their performance can be hindered by complex backgrounds and intra-class variations. This paper proposes a novel segmentation-enhanced classification framework that synergistically combines classical image processing with deep learning. Our approach first employs a fixed-parameter Canny edge detection and contour-based algorithm to segment the WBC foreground. Subsequently, a learnable blending layer intelligently fuses the segmented foreground with the original image, allowing the downstream CNN to leverage both focused object information and contextual cues. We meticulously document our experimental journey, including initial attempts to train Canny parameters which proved unstable. The proposed model, featuring fixed segmentation and a learnable blending factor ( $\alpha$ ), was evaluated on a public blood cell image dataset for cancer detection. It achieved a test accuracy of 99.93%, outperforming a baseline CNN (99.07%). The blending factor $\alpha$ converged to approximately 0.63, indicating an optimal balance between the segmented and original image content. Furthermore, the segmentation-enhanced model demonstrated faster inference times. This work underscores the potential of hybrid approaches, particularly the utility of a learnable blending mechanism to effectively integrate classical segmentation outputs into neural networks for improved classification.