Deep Learning-Based Blood Cell Classification for Enhanced Medical Diagnosis

Abstract

Deep Learning has emerged as a powerful tool in various applications, gaining widespread acceptance over traditional machine learning models. Particularly, the utilization of deep learning algorithms, notably Convolutional Neural Networks (CNN), has significantly advanced the medical field, where large volumes of images require intricate processing and analysis. This paper focuses on developing a deep learning model to tackle the complex blood cell classification problem, a crucial aspect of blood diagnosis. We present a CNN-based framework designed to automatically classify blood cell images into specific subtypes. Comprehensive experiments are conducted on a dataset comprising 13,000 blood cell images and their
corresponding subtypes. The results demonstrate the superiority of our proposed model, showcasing improved performance across various evaluation parameters.