White Blood Cell Segmentation and Determination of Nucleus to Cytoplasm Ratio by using Microscopic Images

This work utilizes a U-Net convolutional neural network for the segmentation of white blood cell (WBC) components, specifically targeting the nucleus and cytoplasm. This work utilizes a U-Net convolutional neural network for the segmentation of white blood cell (WBC) components, specifically targeting the nucleus and cytoplasm. Accurate WBC segmentation is challenging due to differences in cell shape, size, and staining quality. The segmented regions are further used to compute the cytoplasm-to-nucleus (C/N) ratio, which plays a vital role in medical diagnostics. Input images are pre-processed through normalization and resized to a standard dimension of 256 × 256 pixels. Batch normalization is applied to enhance model stability and convergence. The model is trained and deployed using Google Colab, achieving an accuracy of 80%. The proposed framework provides an effective solution for automated analysis of WBC images.