Malaria is one of the epidemics that can cause human death. Accurate and rapid diagnosis of malaria is important for treatment. Due to the limited number of data and human factors, the prediction performance and reliability of traditional classification methods are often affected. In this study, we propose an efficient and novel classification network named Attentive Dense Circular Net (ADCN) which based on Convolutional Neural Networks (CNN). The ADCN is inspired by the ideas of residual and dense networks and combines with the attention mechanism. We train and evaluate our proposed model on a publicly available red blood cells (RBC) dataset and compare ADCN with several well-established CNN models. Compared to other best performing CNN model in our experiments, ADCN shows superiority in all performance criteria such as accuracy (97.47% vs 94.61%), sensitivity (97.86% vs 95.20%) and specificity (97.07% vs 92.87%). Finally, we discuss the obtained results and analyze the difficulties of RBCs classification.

中文翻译：

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一种有效的卷积神经网络，用于对疟疾疾病中的红细胞进行分类。

疟疾是可以导致人类​​死亡的流行病之一。准确，快速地诊断疟疾对治疗很重要。由于数据和人为因素的数量有限，传统分类方法的预测性能和可靠性经常受到影响。在这项研究中，我们提出了一种基于卷积神经网络（CNN）的高效新颖的分类网络，称为注意力密集圆网（ADCN）。ADCN受到残差网络和密集网络概念的启发，并与注意力机制相结合。我们在公开可用的红细胞（RBC）数据集上训练和评估我们提出的模型，并将ADCN与几个公认的CNN模型进行比较。与我们实验中其他性能最佳的CNN模型相比，ADCN在所有性能标准（例如准确度（97.47％对94）中均显示出优势）。61％），敏感性（97.86％vs 95.20％）和特异性（97.07％vs 92.87％）。最后，我们讨论了获得的结果并分析了红细胞分类的困难。