Malaria is a deadly syndrome formed by the Plasmodium parasite that spreads through the bite of infected Anopheles mosquitoes. There are several drugs to cure malaria but it is difficult to detect due to inadequate equipment and technology. Microscopic check-ups of blood smear images by experts help to detect malaria-infected parasites accurately. However, manual analysis is tedious and time-consuming as the experts have to deal with many cases. This paper presents computer assisted malaria parasite detection model by classifying the blood smear image with hybrid deep learning methods that have high accuracy for classification. In the proposed approach the blood smear images are pre-processed using bilateral filtering technique in which features are extracted with the convolutional neural network. These features are selected by the improved grey-wolf optimization, and image classification is performed with the support vector machine. To evaluate the efficiency of the proposed technique, the NIH malaria dataset is utilized and the results are compared with existing approaches in terms of accuracy, F-Measure, recall, precision, and specificity. The outcome reveals that the proposed scheme is accurate and can be more helpful to pathologists for reliable parasite detection.

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