An Efficient Deep Convolutional Neural Network Framework for Automated Multiclass Classifica-tion of White Blood Cells from Microscopic Blood Smear Images
Authors-P.Shilpa, Pooja Pawar
Keyword-decision trees, computer-aided diagnosis, medical image analysis, leukocyte identification.
White blood cell (WBC) subtype identification is essential for the detection of leukemia, infec-tions, immunological deficits, and hematological disorders. When processing a large number of blood smear samples, laboratory specialists' traditional microscopic examination is labor-intensive, time-consuming, and subject to subjective interpretation. An automated multiclass white blood cell categorization system based on Deep Convolutional Neural Networks (CNNs) is presented in this work to overcome these issues. Neutrophils, monocytes, lymphocytes, and eosinophils are the four main leukocyte categories that the suggested approach divides micro-scopic blood smear images into.techniques like scaling, normalization, and dataset splittin g. A deep CNN architecture is used to automatically find discriminative picture features without the need for manually generated feature engineering, while a Decision Tree model functions as a baseline machine learning classifier The suggested CNN model achieves over 97% classification accuracy whereas the Decision Tree classifier achieves roughly 32.2%, demonstrating deep learning's superior ability to extract meaningful visual representations from tiny images, accord-ing to experimental results. By cutting down on analysis time and increasing diagnostic con-sistency,e replacement for computer-aided hematological diagnosis and can greatly improve clinical decision-making for medical professionals.
Doi-[https://doi.org/10.5281/zenodo.21735474]