Blood Cell Image

Blood cell image analysis focuses on automating the identification and classification of different blood cell types from microscopic images, aiming to improve diagnostic accuracy and efficiency in hematology. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) with architectures like ResNet, Inception, and custom-designed models incorporating attention mechanisms and morphological feature extraction, to achieve high accuracy in classifying various white blood cell subtypes and other blood components. This automated approach offers significant potential to reduce human error, increase throughput, and improve the speed and accessibility of blood disease diagnosis, particularly in resource-constrained settings. Furthermore, research is exploring lightweight and explainable models to enhance clinical adoption and trust.