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    A Construction of Object Detection Model for Acute Myeloid Leukemia

    K. Venkatesh1,*, S. Pasupathy1, S. P. Raja2

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 543-560, 2023, DOI:10.32604/iasc.2023.030701

    Abstract The evolution of bone marrow morphology is necessary in Acute Myeloid Leukemia (AML) prediction. It takes an enormous number of times to analyze with the standardization and inter-observer variability. Here, we proposed a novel AML detection model using a Deep Convolutional Neural Network (D-CNN). The proposed Faster R-CNN (Faster Region-Based CNN) models are trained with Morphological Dataset. The proposed Faster R-CNN model is trained using the augmented dataset. For overcoming the Imbalanced Data problem, data augmentation techniques are imposed. The Faster R-CNN performance was compared with existing transfer learning techniques. The results show that the More >

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