Deep Learning Based Automatic Identification of Malaria Infected Cells
The findings indicate that DenseNet121 greatly exceeds the performance of ResNet50 with a test accuracy of 92.15% as opposed to the 70.19% accuracy of ResNet50, thereby showing the benefits of densely connected feature re-use in detecting minute intracellular parasitic features.