Malaria Parasite Enumeration and Classification Using Convolutional Neural Networking

Malaria is a migratory and easily transmissible disease transmitted by virus carrier mosquitoes. Visual quantification and classification of parasitemia in emaciated plasma films is a very tiresome, biased, and time-consuming task. This chapter delivers an unambiguous understanding of the quantification and classification of erythrocytes in tainted wafer-thin blood films diseased with plasmodium parasites. The approach mainly includes microscopic imaging of tainted blood glides, amputation of noise and illumination adjustment, erythrocyte segmentation, morphological operations. By means of the segmented illustrations, parasite density estimation and classification of the stage of infection are done. Two different classification techniques ANN and DCNN have been designed and tested to perform the grouping of diseased erythrocytes into their corresponding stages of growth. The traditional neural network ANN approach gave an accuracy of 93% and this was again overwhelmed by using customized Deep Convolutional Network by achieving an accuracy of 95%.