Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification

Abstract

A. lumbricoides infection affects up to 1/3 of the world population (approximately 1.4 billion people worldwide). It has been estimated that 1.5 billion cases of infection globally and 65,000 deaths occur due to A. lumbricoides. Generally, allied health classifies parasite egg type by using on microscopy-based methods that are laborious, are limited by low sensitivity, and require high expertise. However, misclassification may occur due to their heterogeneous experience. For their reason, computer technology is considered to aid humans. With the benefit of speed and ability of computer technology, image recognition is adopted to recognize images much more quickly and precisely than human beings. This research proposes deep learning for A. lumbricoides's egg image recognition to be used as a prototype tool for parasite egg detection in medical diagnosis. The challenge is to recognize 3 types of eggs of A. lumbricoides with the optimal architecture of deep learning. The results showed that the classification accuracy of the parasite eggs is up to 93.33%. This great effectiveness of the proposed model could help reduce the time-consuming image classification of parasite egg.