HematoFusion: A Weighted Residual-Vision Transformer Ensemble for Automated Classification of Haematologic Disorders in Microscopic Blood Images - Saadallah, Mouna; Oulladji, Latefa; Ben-Naoum, Farah (Informatica, 2025)
Haematologic malignancies pose a significant global challenge, with 1.34 million new cases reported in 2019 and leukemia claiming 311,594 lives in 2020. Early diagnosis of these blood disorders increases survival chances by enabling prompt treatment, yet their complexity and variable cellular morphology hinder accurate detection. Advances in Medical Imaging and AI, particularly Image Classification, offer solutions by analyzing blood samples for subtle morphological patterns. This study advances the field by introducing a novel data set for the classification of red blood cells and using open-source data for the classification of leukemia and lymphoma (each covering 29,363; 16,811; and 1,436 images, respectively). We fine-tuned multiple AI models, including EfficientNetB3, ResNet50V2, and a pretrained Vision Transformer (ViT), and combined their strengths into a weighted ensemble framework. Evaluated across various metrics (including accuracy, precision, recall, etc.), the proposed HematoFusion model excelled, achieving 96% accuracy in the morphology of red blood cells, 99% in Leukemia, and 96% in Lymphoma, surpassing most existing models in terms of accuracy while covering a wider range of haematologic disorders. These findings demonstrate the potential of integrated AI frameworks to improve haematologic diagnostics with precision and reliability.