MALARIA DISEASE RECOGNITION THROUGH ADAPTIVE DEEP LEARNING MODELS
Sammy V. Militante · Journal of Critical Reviews · 2020
Malaria is a disease from blood generated by the Plasmodium parasites spread through the bite of female Anopheles mosquito. Inspection thru specialists is done on the dense, fine blood slurs to make a diagnosis and perform parasitemia calculations. Nevertheless, precision testing varies on slur characteristics and technical skills in tallying and rating infected cells. It could be laborious for a massive population in conducting assessment given the poor conditions and locality. Convolutional Neural Networks, a division of deep learning (DL) models has dominance and extensible result in terms of feature extraction and classification. Automatic screening of malaria with deep learning techniques is an effective tool in analyzing the malaria. The author has evaluated the implementations of the pre-trained convolutional neural network-based Deep Learning models as feature extractors in categorizing and improving the examination of cells of infected and not-infected with malaria.