Convolutional Neural Networks Based White Blood Cell Classification Method from Digital Holographic Microscope Images
Sanem Uytan, Selin Necip, G. Bora Esmer · 2025
In the presented work, an innovative method is proposed for the classification of white blood cells (WBCs) using digital holography microscopy (DHM) images. The developed approach uses a peripheral blood cell dataset to automatically and accurately classify WBCs. The classification process is performed based on the calculated features and these features are obtained by processing the DHM images via a convolutional neural network (CNN) architecture. By integrating DHM with image processing, we propose an efficient and highly accurate WBC analysis method. The performance of the proposed method is evaluated according to the F1score and precision metrics. The proposed method gives a test accuracy of 96.45%, an F1score of 96.44%, and a precision metric of 96.44% for the ResNeXt model, which yielded the best results among the tested architectures.