Transfer Learning based Classification of Plasmodium Falciparum Parasitic Blood Smear Images

A transfer learning-based convolutional neural network (CNN) architecture is used in the current study to differentiate parasitic malaria cell images from the healthy ones and localize the parasites in infected images using global average pooling(GAP) and heat map. Malaria is a serious malady that can even lead to death in the absence of timely diagnosis. With the use of computerized malaria diagnosis, the suggested solution tackles the problem of timely detection and eases the strain on health care. Three transfer learning-based neural network architectures are studied and compared in terms of their accuracy, precision, sensitivity and specificity. The optimal model with less number of false negatives was then interfaced with a newly developed web service which can be easily accessed and used by common people. The studied models were trained and evaluated on 27,558 single cell images, yielding a maximum accuracy of 96.88%, with 97.35% sensitivity, 96.41% specificity, 96.89% F1-Score, and 96.44% precision.