DEEP LEARNING FOR SEGMENTATION AND COUNTING OF WHITE BLOOD CELLS IN CLINICAL DIAGNOSIS

Abstract

In the field of cytopathology, the accurate identification and counting of white blood cells (WBCs) in blood smears is crucial for diagnosing various types of cancer. The process of manually detecting and segmenting these structures, however, can be challenging due to their variable morphologies and the presence of overlapping objects in the images. This makes manual detection time-consuming, labor-intensive, and prone to error, particularly for individuals without extensive experience in cytopathology. In this paper, a deep learning algorithm is developed based on a Mask R-CNN model and driven by a sub-algorithm called KOWN (Keep Only White Blood Cells with Nuclei) for WBC segmentation and counting. The proposed algorithm improves the accuracy of measurements compared to other rapidly growing deep learning works, providing maximum precision in detecting and counting WBCs in both low- and high-blood-cell-density images.