White Blood Cell Classification using Sequential Convolutional Neural Network Transfer Learning Model

The classification of blood cells is essential because it gives an assay that may be used in the diagnosis of a variety of diseases. In order to get beyond the difficulty and imprecision of the conventional approaches that are based on light microscopy, The construction and testing of a deep learning system that can accurately and quickly classify different types of blood cells is the primary focus of this study. Our model achieves a remarkable overall accuracy of 99% by using a huge and diversified dataset. This is a significant milestone in the field of medical picture analysis. The sequential CNN architecture exhibits its capacity to learn complex characteristics and patterns within blood cell pictures, exceeding other methods and establishing a new benchmark for the accuracy of categorization. The efficacy of sequential CNN algorithms, as well as the future of blood cell analysis, both hold the possibility of better diagnostic precision, which will ultimately lead to improved quality of life outcomes.