Blood Cell Classification

Blood cell classification aims to automate the identification of different blood cell types from microscopic images, significantly accelerating and improving the accuracy of hematological diagnoses. Current research focuses on developing robust deep learning models, including convolutional neural networks (CNNs) like ResNet and EfficientNet, graph attention networks (GATs), and novel architectures incorporating attention mechanisms and feature fusion techniques to address challenges like domain shifts and data imbalance. These advancements hold significant promise for improving the speed and accuracy of disease diagnosis, particularly in identifying hematological malignancies like leukemia, and enabling more efficient and reliable clinical workflows.