Malaria is a serious disease responsible for thousands of deaths each year. Many efforts have been made to aid in the diagnosis of malaria using machine learning techniques, but to date, the presence of other elements that may interfere with the recognition of malaria has not been considered. We have developed the first deep learning model using convolutional neural networks capable of differentiating malaria-infected red blood cells from not only normal erythrocytes but also erythrocytes with other types of inclusions. 6415 images of red blood cells were segmented from digital images of 53 peripheral blood smears using thresholding and watershed transformation techniques. These images were used to train a VGG-16 architecture using transfer learning. Using an independent test set of 23 smears, this model was 99.5% accurate in classifying malaria parasites and other red blood cell inclusions. This model also exhibited sensitivity and specificity values of 100% and 91.7%, respectively, classifying a complete smear as infected or not infected. Our model represents a promising advance for automation in the identification of malaria-infected patients. The differentiation between malaria parasites and other red blood cell inclusions demonstrates the potential utility of our model in a real work environment.

疟疾是一种严重的疾病，每年造成数千人死亡。已经做出了许多努力来使用机器学习技术帮助诊断疟疾，但迄今为止，尚未考虑可能干扰疟疾识别的其他因素的存在。我们开发了第一个使用卷积神经网络的深度学习模型，该模型不仅能够区分受疟疾感染的红细胞与正常红细胞，还能够区分具有其他类型内含物的红细胞。使用阈值和分水岭转换技术从 53 份外周血涂片的数字图像中分割出 6415 份红细胞图像。这些图像用于使用迁移学习训练 VGG-16 架构。使用 23 个涂片的独立测试集，该模型为 99。对疟疾寄生虫和其他红细胞内含物进行分类的准确率为 5%。该模型还表现出分别为 100% 和 91.7% 的灵敏度和特异性值，将完整的涂片分类为感染或未感染。我们的模型代表了疟疾感染患者识别自动化方面的一个有希望的进步。疟原虫和其他红细胞内含物之间的区别证明了我们的模型在实际工作环境中的潜在效用。