JCVirusNEntamoebaNET (JCENET): A Deep Neural Network for John Cunningham Virus and Entamoeba Parasite Detection

摘要: Parasites and viruses are responsible for attacking the immune system of human body destroying it in process which makes detection parasites virus a penultimate task modern medical analysis. The is often characterized with symptoms, microscopic tests. However, little research work has been carried out to exploit garner capability deep learning classify detect virus. In our work, we propose new neural network based image processing approach parasites, germs case Entamoeba parasite leads Amebiasis disease John Cunningham Progressive multifocal leukoencephalopathy (PML). We have applied own convolutional JC entamoeba using immunohistochemistry(IHC) images microscope from dataset that collected various neuropathology laboratories, researchers. Our model achieved overall classification accuracy 77% F1-score 76% strong narrative rationale utilization batch normalization dropout layer model.