Transfer Learning with ResNet-50 for Malaria Cell-Image Classification 论文

摘要

Malaria is an infectious disease caused by single-celled parasite of plasmodium group. The disease is more often spread by an Infected Female Anopheles mosquito. In 2017 alone 219 million cases and nearly 435,000 deaths were reported, with more than 40% of global population at risk. In spite of many advanced evaluation techniques for identifying the infection, microscopists at resource constrained regions face challenge in improving the diagnostic accuracy. Deep learning based classification of cell images prevent the wrong diagnostic decisions. This paper focuses on the implementation of Transfer learning based classification of malarial infected cells to improve the diagnostic accuracy. The experimental results show that transfer learning performs well on microscopic cell-images.