Malaria is a parasitic infection caused in humans by the parasite belonging to the genus Plasmodium. The
traditional approach of diagnosis by the use of microscopy, considered to be the “gold standard method” has at times proved to
be inefficacious and inefficient as it is time consuming, needs more expertise and is erring at times. This raises a need for
better alternatives for the diagnosis of the parasitic infection. This paper highlights the advancement in the field of machine
learning and its beneficial applications in the detection, identification and diagnosis of the malarial infection via the use of
smartphones. It uses a pre trained CNN for the detection of the parasite. The experimental results excelled in the relevant
attributes of accuracy, efficiency and sensitivity of this technique, making this outperform the traditional means of detection.
