Malaria is a contagious disease that affects millions of lives every year. Traditional diagnosis of malaria in laboratory requires an experienced person and careful inspection to discriminate healthy and infected red blood cells (RBCs). It is also very time-consuming and may produce inaccurate reports due to human errors. Cognitive computing and deep learning algorithms simulate human intelligence to make better human decisions in applications like sentiment analysis, speech recognition, face detection, disease detection, and prediction. Due to the advancement of cognitive computing and machine learning techniques, they are now widely used to detect and predict early disease symptoms in healthcare field. With the early prediction results, healthcare professionals can provide better decisions for patient diagnosis and treatment. Machine learning algorithms also aid the humans to process huge and complex medical datasets and then analyze them into clinical insights. This paper looks for leveraging deep learning algorithms for detecting a deadly disease, malaria, for mobile healthcare solution of patients building an effective mobile system. The objective of this paper is to show how deep learning architecture such as convolutional neural network (CNN) which can be useful in real-time malaria detection effectively and accurately from input images and to reduce manual labor with a mobile application. To this end, we evaluate the performance of a custom CNN model using a cyclical stochastic gradient descent (SGD) optimizer with an automatic learning rate finder and obtain an accuracy of 97.30% in classifying healthy and infected cell images with a high degree of precision and sensitivity. This outcome of the paper will facilitate microscopy diagnosis of malaria to a mobile application so that reliability of the treatment and lack of medical expertise can be solved.

中文翻译：

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利用深度学习技术通过移动应用程序检测疟疾寄生虫

疟疾是一种传染性疾病，每年影响数百万人的生命。在实验室中对疟疾的传统诊断需要经验丰富的人员和仔细的检查来区分健康和感染的红细胞（RBC）。这也是非常耗时的，并且由于人为错误可能会生成不准确的报告。认知计算和深度学习算法可模拟人类智能，从而在情感分析，语音识别，面部检测，疾病检测和预测等应用中做​​出更好的人类决策。由于认知计算和机器学习技术的进步，它们现在被广泛用于检测和预测医疗领域的早期疾病症状。利用早期的预测结果，医疗保健专业人员可以为患者的诊断和治疗提供更好的决策。机器学习算法还可以帮助人们处理庞大而复杂的医学数据集，然后将其分析为临床见解。本文寻求利用深度学习算法来检测致命疾病，疟疾，为构建有效移动系统的患者提供移动医疗解决方案。本文的目的是展示深度学习体系结构（例如卷积神经网络（CNN））如何有效，准确地从输入图像中实时检测疟疾，并减少移动应用程序的人工劳动。为此，我们使用带有自动学习率查找器的周期性随机梯度下降（SGD）优化器评估自定义CNN模型的性能，并获得97的准确性。高度准确和灵敏地对健康和感染细胞图像进行分类的30％。该论文的这一成果将有助于在移动应用中对疟疾进行显微镜诊断，从而可以解决治疗的可靠性和缺乏医学专业知识的问题。