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@article{177278,
        author = {Cirigiri Akanksha and Erpula Shiva Kumar and Nalgonda Yogeshwar and Narala Abhyas and Mr. Mohammed Afzal and Dr. M. Ramesh},
        title = {AI-Driven Classification From Peripheral Blood Cell Images For Hematological Disorders},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {403-408},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177278},
        abstract = {Peripheral blood cell analysis plays a vital role in diagnosing various hematological disorders. Traditional computational models primarily focus on counting blood cells, but accurate classification of different blood cell types is crucial for reliable diagnosis. In this work, we propose a deep learning-based approach for classifying red blood cells (RBCs), platelets, and different white blood cell (WBC) subtypes, including eosinophils, basophils, lymphocytes, and others, from peripheral blood smear images. Using a convolutional neural network (CNN) architecture trained on the Blood Cell Count and Detection (BCCD) dataset, we achieve a high classification accuracy of approximately 80%. Our model demonstrates strong potential in automating hematological analysis and improving diagnostic efficiency. This study emphasizes the effectiveness of AI-driven solutions in enhancing the precision and reliability of blood cell classification.},
        keywords = {Blood Cell Classification, Deep Learning, Hematological Disorders, Medical Image Analysis, White Blood Cells.},
        month = {April},
        }