AI-assisted diagnosis of anemia through peripheral smear image analysis: A cross-validation study

A deep semi-supervised learning model for automating anemia detection and classification from peripheral blood smear images is of interest. A convolutional neural network was trained on 3,200 images, with only 25% annotated by expert hematologists. The model achieved a classification accuracy of 93.4% and F1-scores above 90% for key anemia subtypes, demonstrating strong agreement with expert diagnoses (κ = 0.89). It significantly reduced diagnostic time and performed well in detecting microcytic and sickle cell anemia. This AI-based framework shows great potential for accurate anemia diagnosis, especially in resource-limited settings.