Automatic Detection of Plasmodium Parasite Using Convolution Neural Network

The health industry is quite active and malaria is the deadliest disease on earth. A laboratory or skilled technician will often do a schematic analysis of a patient's blood film under a microscope to look for parasite-infected red blood cells. The procedure is ineffective, and the diagnosis is based on the expertise and experience of the individuals who will be performing the examination. Malaria diagnosis using a blood smear has already been carried out using deep learning algorithms. The suggested method uses image-based diagnosis with CNNs and VGG models to accomplish this goal, yielding a high accuracy of 94.7% and precision in differentiating infected and uninfected cells.