White Blood Cell Classification Using Pre-Trained Deep Neural Networks and Transfer Learning

The accurate classification of white blood cells is crucial for diagnosing and monitoring various medical conditions. Traditional methods of diagnosis are expensive, time-consuming, and depend on specialists' expertise. Image processing-based techniques provide a faster and simpler approach to detecting abnormalities in white blood cells. In this study, a computer-aided diagnosis system was proposed that utilizes pre-trained networks for accurate classification of white blood cells. The dataset used in this study is sourced from Kaggle, and the classification process is performed without image segregation or feature extraction techniques. Pretrained series networks are employed for classification, and a classification accuracy of 99% is achieved using the Inception-ResNet-v2 network with the Adam optimizer. The comparative analysis aids in understanding the performance and suitability of different network architectures for white blood cell classification tasks. The results showcase the potential of the proposed computer-aided diagnosis system for accurate and efficient white blood cell classification, contributing to advancing the automation and reliability of white blood cell analysis and supporting improved disease diagnosis and monitoring.