Residual Block-Driven CNN for Accurate White Blood Cell Image Analysis and Classification

White blood cells are crucial for our immune system because they offer protection against the outer body substances and other hostile organisms. WBC serves different purposes in the body’s overall immune response. All sorts of precise WBC counts are frequently used in diagnostics to check the proper proportions of different WBC types and help identify possible health problems. Proper diagnosis of WBCs is essential for diagnosing various diseases. This present study shows an optimized road ma using residual block-driven convolutional neural network(CNN) architecture to improve WBC classification. Our method of detection of WBC improves the efficiency of pre-train models to make the training fast while proper extraction of detailed features. This proposed method exceeds traditional ways by fine-tuning the network and implementing residual blocks. This amended framework suggests the potential to enhance the diagnostic accuracy of 99.20% for ResNet50 and efficiency in WBC analysis, giving a robust tool for medical diagnostics.