  Malaria is a female anopheles mosquito-bite inflicted life-threatening
disease which is considered endemic in many parts of the world. This article
focuses on improving malaria detection from patches segmented from microscopic
images of red blood cell smears by introducing a deep convolutional neural
network. Compared to the traditional methods that use tedious hand engineering
feature extraction, the proposed method uses deep learning in an end-to-end
arrangement that performs both feature extraction and classification directly
from the raw segmented patches of the red blood smears. The dataset used in
this study was taken from National Institute of Health named NIH Malaria
Dataset. The evaluation metric accuracy and loss along with 5-fold cross
validation was used to compare and select the best performing architecture. To
maximize the performance, existing standard pre-processing techniques from the
literature has also been experimented. In addition, several other complex
architectures have been implemented and tested to pick the best performing
model. A holdout test has also been conducted to verify how well the proposed
model generalizes on unseen data. Our best model achieves an accuracy of almost
97.77%.
