  Malaria, a life-threatening disease, infects millions of people every year
throughout the world demanding faster diagnosis for proper treatment before any
damages occur. In this paper, an end-to-end deep learning-based approach is
proposed for faster diagnosis of malaria from thin blood smear images by making
efficient optimizations of features extracted from diversified receptive
fields. Firstly, an efficient, highly scalable deep neural network, named as
DilationNet, is proposed that incorporates features from a large spectrum by
varying dilation rates of convolutions to extract features from different
receptive areas. Next, the raw images are resampled to various resolutions to
introduce variations in the receptive fields that are used for independently
optimizing different forms of DilationNet scaled for different resolutions of
images. Afterward, a feature fusion scheme is introduced with the proposed
DeepFusionNet architecture for jointly optimizing the feature space of these
individually trained networks operating on different levels of observations.
All the convolutional layers of various forms of DilationNets that are
optimized to extract spatial features from different resolutions of images are
directly transferred to provide a variegated feature space from any image.
Later, joint optimization of these spatial features is carried out in the
DeepFusionNet to extract the most relevant representation of the sample image.
This scheme offers the opportunity to explore the feature space extensively by
varying the observation level to accurately diagnose the abnormality. Intense
experimentations on a publicly available dataset show outstanding performance
with accuracy over 99.5% outperforming other state-of-the-art approaches.
