Abstract
Malaria is still a serious health problem globally, which is caused by a parasite of genus Plasmodium through Anopheles mosquitoes. An automated and accurate diagnosis is needed for an appropriate intervention to reduce mortality and to prevent anti-malaria resistance. In general, pathologists investigate blood-stained slides through conventional microscopy for diagnosis. However, these approaches are done by a clinical expert which biased, error-prone and moderate. This research addresses the development of robust computer-assisted malaria diagnosis in light microscopic blood images. In addition, microscopic images were obtained through stained slides which consist of illuminations and noise levels. To overcome this situation used computer vision approach i.e., improved k-SVD denoising method, fuzzy type II-based segmentation, feature extraction through local and global features, feature selection with Extra Trees Classifier and classify the different stages of malaria have been explored. The proposed classification strategy can be achieved by unifying Extremely Randomized Trees (ERT). The obtained experimental results showed Extremely Randomized Trees classifier accuracy of 98.02% that have been achieved in the process of malaria diagnosis.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.