A Deep learning approach for anemia detection using peripheral blood smear images

Anemia remains one of the most prevalent public health conditions globally, and accurately identifying its morphological subtype is crucial for appropriate clinical management. Conventional diagnosis involves the microscopic assessment of peripheral blood smears by a trained hematologist or pathologist to classify red blood cells (RBCs) based on cellular size and shape, a process that is time-consuming, subjective, and constrained by the limited availability of expert personnel, particularly in low-resource healthcare environment. In response, this paper introduces a deep learning-based framework for automated classification of RBC morphology directly from peripheral blood smear microscopy images into three clinically relevant classes: Normocyte, Macrocyte, and Microcyte. We construct and preprocess a dataset containing 2,500 RBC images and apply preprocessing class-balancing augmentation to address dataset imbalance. Three transfer-learning-based architectures. ResNet50, DenseNet121, and a Vision Transformer (ViT-Base), are individually fine-tuned and evaluated, achieving individual test accuracies of 89.87%, 88.53%, and 89.07% respectively, with mean AUC scores above 0.96 in all cases. We further propose a performance-weighted hybrid ensemble that fuses the softmax outputs of all three models, improving test accuracy to 91.20% and mean AUC to 0.9792, outperforming every individual architecture. These results illustrate that combining convolution-based and attention-based feature representations improves robustness in fine-grained RBC morphology classification, and suggest a viable pathway toward low-cost, automated anemia subtyping to support diagnosis in settings where expert microscopy review is limited.