Childhood leukaemia demands meticulous blood cell analysis for diagnosis, focusing on morphological irregularities like asymmetry and abnormal cell counts. Traditional manual diagnosis via microscopic blood smear images suffers from reduced reliability, time intensiveness, and observer variability. Computer-aided diagnostic (CAD) systems address these challenges. Integrating real-time image pre-processing and segmentation ensures swift operation, reducing the CAD system processing time. This enhances its overall effectiveness, enabling timely medical intervention and better patient outcomes. This study aims to simplify the algorithmic complexity of pre-processing steps, including bilateral filtering and Contrast-Limited Adaptive Histogram Equalization (CLAHE), alongside the segmentation stage involving morphological operations and the watershed algorithm. This work proposes a parallel implementation utilizing OpenMP and CUDA, evaluating its performance using accuracy and Intersection over Union (IoU) metrics along with computing time and algorithmic complexity. It highlights the benefits of parallel processing in enhancing efficiency and and accuracy in blood cell analysis.
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