Automated Detection of White Blood Cells and Platelets From Microscopic Images using Deep Learning Models

Abstract

This study focuses on the detection of white blood cells (WBCs) and platelets using deep learning( DL) models on microscopic image data. The accurate identification and categorization of these blood cell types are essential for various medical diagnostic applications. The proposed methodology includes data preprocessing and inputting the preprocessed data into the model. The experimental results demonstrate that the proposed method employed in this study has the potential to achieve mAP@0.5 of 0.989 and mAP@0.5:0.95 of 0.909, indicating highly accurate detection of white blood cells and platelets. Experimental results show the approach's efficacy in accurately detecting WBCs and platelets. The findings highlight the potential of our approach in automating the analysis of microscopic images and contribute to the advancement of medical diagnostics in this domain.