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This paper presents a novel approach to leukemia detection in blood samples using advanced technologies such as medical imaging and machine learning. Leveraging Convolutional Neural Networks (CNNs) and the MobileNetV2 architecture, the study develops an integrated predictive model trained on a diverse dataset of blood smear images. Hardware integration, particularly utilizing Raspberry Pi, facilitates efficient image processing and analysis. Rigorous testing ensures the system's accuracy and reliability, offering advantages in enhanced diagnostic accuracy, speed, and user-friendly interface. Despite challenges such as data variability and ethical considerations, the proposed approach demonstrates promise for improving patient outcomes and healthcare efficiency.
Bhagirathi et al. (Wed,) studied this question.