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  • Open Access

    ARTICLE

    A Lightweight Explainable Deep Learning for Blood Cell Classification

    Ngoc-Hoang-Quyen Nguyen1, Thanh-Tung Nguyen2, Anh-Cang Phan3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2435-2456, 2025, DOI:10.32604/cmes.2025.070419 - 26 November 2025

    Abstract Blood cell disorders are among the leading causes of serious diseases such as leukemia, anemia, blood clotting disorders, and immune-related conditions. The global incidence of hematological diseases is increasing, affecting both children and adults. In clinical practice, blood smear analysis is still largely performed manually, relying heavily on the experience and expertise of laboratory technicians or hematologists. This manual process introduces risks of diagnostic errors, especially in cases with rare or morphologically ambiguous cells. The situation is more critical in developing countries, where there is a shortage of specialized medical personnel and limited access to… More > Graphic Abstract

    A Lightweight Explainable Deep Learning for Blood Cell Classification

  • Open Access

    ARTICLE

    Qualitative Abnormalities of Peripheral Blood Smear Images Using Deep Learning Techniques

    G. Arutperumjothi1,*, K. Suganya Devi2, C. Rani3, P. Srinivasan4

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1069-1086, 2023, DOI:10.32604/iasc.2023.028423 - 06 June 2022

    Abstract In recent years, Peripheral blood smear is a generic analysis to assess the person’s health status. Manual testing of Peripheral blood smear images are difficult, time-consuming and is subject to human intervention and visual error. This method encouraged for researchers to present algorithms and techniques to perform the peripheral blood smear analysis with the help of computer-assisted and decision-making techniques. Existing CAD based methods are lacks in attaining the accurate detection of abnormalities present in the images. In order to mitigate this issue Deep Convolution Neural Network (DCNN) based automatic classification technique is introduced with… More >

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