DIFFUSION-BASED GENERATIVE MODELS FOR BLOOD CELL MORPHOLOGY CLASSIFICATION

Author

DR. ANITA PAL,SWATI MATHUR

Abstract

 Blood cell morphology analysis plays a vital role in the diagnosis and monitoring of hematological disorders such as anemia, leukemia, infections, and other blood-related diseases. Conventional microscopic examination performed by hematologists is time-consuming, subjective, and prone to inter-observer variability, highlighting the need for automated and reliable diagnostic systems. Recent advances in artificial intelligence, particularly deep learning, have significantly improved the accuracy of medical image classification. However, conventional discriminative models often struggle with limited labeled datasets, class imbalance, and poor generalization to unseen blood cell variations. This research proposes a Deep Generative Classification framework for blood cell morphology using generative deep learning techniques to enhance classification performance and robustness. The proposed approach integrates image preprocessing, feature extraction, latent representation learning, and generative modeling to accurately classify different blood cell types while generating realistic synthetic samples for data augmentation. The framework is evaluated using publicly available blood cell image datasets and assessed through performance metrics such as accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Experimental results indicate that the proposed model achieves superior classification accuracy and improved generalization compared with conventional convolutional neural network (CNN)-based approaches. The proposed system has significant potential for assisting hematologists in clinical diagnosis, reducing diagnostic errors, supporting early disease detection, and improving healthcare decision-making through intelligent computer-aided diagnostic systems.

Keywords

Deep Generative Models; Blood Cell Morphology; Medical Image Classification; Deep Learning; Artificial Intelligence; Diffusion Models; Convolutional Neural Networks (CNN); Hematology;

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References

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