Lightweight Deep Learning for Malaria Parasite Detection Using Cell-Image of Blood Smear Images
Amin Alqudah, Ali Mohammad Alqudah, Shoroq Qazan
Abstract
Malaria is an infectious disease that is caused by the plasmodium parasite which is a single-celled group. This disease is usually spread employing an infected female anopheles mosquito. Recent statistics show that in 2017 there were only around 219 million recorded cases and about 435,000 deaths were reported due to this disease and more than 40% of the global population is at risk. Despite this, many image processing fused with machine learning algorithms were developed by researchers for the early detection of malaria using blood smear images. This research used a new CNN model using transfer learning for classifying segmented infected and Uninfected red blood cells. The experimental results show that the proposed architecture success to detect malaria with an accuracy of 98.85%, sensitivity of 98.79%, and a specificity of 98.90% with the highest speed and smallest input size among all previously used CNN models.