Blood Smear

Blood smear analysis, a cornerstone of hematological diagnosis, is undergoing a transformation driven by advancements in computer-aided diagnosis (CAD). Current research focuses on developing and refining deep learning models, particularly convolutional neural networks (CNNs) like ResNet50 and U-Net, to automate the identification and classification of various blood cells, including those indicative of diseases such as malaria and leukemia. These automated systems aim to improve diagnostic accuracy, speed, and accessibility, ultimately impacting healthcare by reducing the workload on pathologists and enabling faster, more efficient diagnoses in resource-limited settings. The emphasis is on improving model generalization across diverse datasets and incorporating expert knowledge into model design to enhance reliability and clinical applicability.