Malaria is a life-threatening mosquito (Anopheles)-borne blood disease caused by the plasmodium parasite. Microscopic examination of peripheral blood smears by experts helps to identify parasites precisely. The manual assessment technique is a tedious and time-consuming process. The present study focuses on developing a hybrid screening algorithm for automated identification and classification of malaria parasite-infected red blood cells (RBCs). Initially, a semantic blood cell segmentation method is adopted where a supervised classifier-regulated pixel-based segmentation is adopted to segment individual RBC present in an image. In pixel-based classification, foreground (RBCs) and background regions are considered, a pixel-based large feature dataset is generated, and an artificial neural network (ANN) classifier is trained. The trained model generates a probability map of an image which is later post-processed by Graph-cut and Marker-controlled Watershed method for developing cropped RBC image set. The proposed segmentation method achieves 99.1% accuracy. Finally, a trained modified Capsule Network (CapsNet) model is used for classification of segmented blood cells to identify the species and stages of the parasites. Here, two specific parasite species viz., Plasmodium vivax and Plasmodium falciparum with stages are considered for classification. The performance of the proposed two-steps hybrid malaria screening is promising and the training and testing on local and benchmark dataset with respect to ground truth yield 98.7% accuracy.

疟疾是由疟原虫寄生虫引起的威胁生命的蚊子（按蚊）传播的血液病。专家对外周血涂片进行显微镜检查有助于准确鉴定出寄生虫。手动评估技术是一个繁琐且耗时的过程。本研究的重点是开发一种混合筛选算法，用于疟疾寄生虫感染的红细胞（RBC）的自动识别和分类。最初，采用语义血细胞分割方法，其中采用监督的分类器调节的基于像素的分割来分割图像中存在的单个RBC。在基于像素的分类中，考虑前景（RBC）和背景区域，生成基于像素的大特征数据集，并训练一个人工神经网络（ANN）分类器。训练后的模型生成图像的概率图，该图像随后通过图割和标记控制的分水岭方法进行后处理，以开发裁剪的RBC图像集。提出的分割方法可达到99.1％的准确度。最后，使用经过训练的改良胶囊网络（CapsNet）模型对分段的血细胞进行分类，以鉴定寄生虫的种类和阶段。在这里，两个特定的寄生虫物种，即间期的间日疟原虫和恶性疟原虫考虑用于分类。拟议的两步混合疟疾筛查的性能是有前途的，并且在本地和基准数据集上关于地面真相的训练和测试的准确性为98.7％。