Deep Learning Approach for Ascaris lumbricoides Parasite Egg Classification.

Narut Butploy,Wanida Kanarkard,Pewpan Maleewong Intapan,Lizandra Guidi Magalhães

doi:10.1155/2021/6648038

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.

Highlights

Intestinal parasites are among the main public health problems around the world especially in tropical and subtropical countries [1]
The results showed that convolutional neural network (CNN) are more accurate than both Naïve Bayes (NB) and Support Vector Machine (SVM)
Supporting resources must be shared between the central processing units (CPUs) and the graphics processing units (GPUs)

Summary

Introduction

Intestinal parasites are among the main public health problems around the world especially in tropical and subtropical countries [1]. Ascaris lumbricoides is a nematode parasite that causes the common tropical infection ascariasis in humans [2]. This parasite causes harmfully infection in human digestive tract. There are three forms of eggs: fertile, decorticate, and infertile. Fertile eggs are oval in shape, measuring 40×60 μm. The egg is termed decorticate if the external albuminous layer is absent. Infertile eggs are larger, measuring 60×90 μm and more elongated in shape, have a thinner shell, and are poorly organized internally, being a mass of variably sized granules. Nkamgang et al [4] detect and automatically detect intestinal parasites by neuro-fuzzy system

Methods

Results

Conclusion