Abstract: Malaria is a very extreme infectious disease caused by the genus Plasmodium peripheral blood parasite. Occasionally, traditional microscopy, which is now "the gold standard" for the diagnosis of malaria, has proven ineffective as it requires time and findings are difficult to replicate. Automation of the assessment process is of high significance because it presents a significant global health concern. In this work, an accurate, quick and affordable malaria diagnosis model was established using stained thin blood smear images. A collection of intensity-based features was suggested and the performance of these features was assessed using an neural network classifier on the red blood cell samples from the generated database.
Keywords: malaria disease, pre-processing, classifier algorithm, feature extraction, Convolutional Neural Network (CNN) etc.
| DOI: 10.17148/IJARCCE.2021.10585