  The advent of Deep Learning models like VGG-16 and Resnet-50 has considerably
revolutionized the field of image classification, and by using these
Convolutional Neural Networks (CNN) architectures, one can get a high
classification accuracy on a wide variety of image datasets. However, these
Deep Learning models have a very high computational complexity and so incur a
high computational cost of running these algorithms as well as make it hard to
interpret the results. In this paper, we present a shallow CNN architecture
which gives the same classification accuracy as the VGG-16 and Resnet-50 models
for thin blood smear RBC slide images for detection of malaria, while
decreasing the computational run time by an order of magnitude. This can offer
a significant advantage for commercial deployment of these algorithms,
especially in poorer countries in Africa and some parts of the Indian
subcontinent, where the menace of malaria is quite severe.
