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Deep Learning Based Approach For Malaria Detection in Blood Cell Images

机译:基于深度学习的血细胞图像疟疾检测方法

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Malaria, a life-threatening disease, develops due to the bite of female Anopheles mosquito. It spreads the plasmodium parasites in human blood, killing hundreds of millions of people every year. Modern scientific advancements play a pivotal role to combat the disease, along with biomedical research by the medical experts to possibly eradicate this disease from all parts of the world. With the significant development in deep learning research, faultless identification of medical imaging has become an important factor in medical diagnosis and decision-making. To this end, we present a deep learning based approach using a convolutional neural network for detecting malaria from microscopic cell images using image classification. The proposed CNN model implemented using 5-fold cross validation approach outperforms all the existing methods in terms of accuracy and other evaluation metrics, thus achieving the best results till date in malaria detection using deep learning.
机译:疟疾,危及生命的疾病,由于女性粪便蚊子叮咬而发展。它将疟原虫在人体血液中蔓延,每年杀死数亿人。现代科学进步发挥了枢轴作用来打击这种疾病,以及医学专家的生物医学研究可能会从世界各地消除这种疾病。随着深度学习研究的重大发展,医学成像的故障鉴定已成为医学诊断和决策的重要因素。为此,我们使用卷积神经网络呈现基于深度学习的方法,用于使用图像分类检测疟疾的疟疾。利用5倍交叉验证方法实施的提议的CNN模型在准确性和其他评估指标方面优于所有现有方法,从而实现了使用深度学习的疟疾检测中的最佳结果。

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