Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework

arXiv:2212.02477v3 Announce Type: replace-cross
Abstract: Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and millions worldwide yearly. However, specialists' manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boosted-BR-STM convolutional neural networks (CNN) and the ensemble ML classifiers, is developed to screen malaria parasite images. The proposed Boosted-BR-STM is based on a new dilated-convolutional …

abstract arxiv cells clinical cs.cv cs.lg detection eess.iv ensemble error framework however malaria novel practice screening type