Helminthiasis disease is one of the most serious health problems in the world and frequently occurs in children, especially in unhygienic conditions. The manual diagnosis method is time consuming and challenging, especially when there are a large number of samples. An automated system is acknowledged as a quick and easy technique to assess helminth sample images by offering direct visibility on the computer monitor without the requirement for examination under a microscope. Thus, this paper aims to compare the human intestinal parasite ova segmentation performance between machine learning segmentation and deep learning segmentation. Four types of helminth ova are tested, which are Ascaris Lumbricoides Ova (ALO), Enterobious Vermicularis Ova (EVO), Hookworm Ova (HWO), and Trichuris Trichiura Ova (TTO). In this paper, fuzzy c-Mean (FCM) segmentation technique is used in machine learning segmentation, while convolutional neural network (CNN) segmentation technique is used for deep learning. The performance of segmentation algorithms based on FCM and CNN segmentation techniques is investigated and compared to select the best segmentation procedure for helminth ova detection. The results reveal that the accuracy obtained for each helminth species is in the range of 97% to 100% for both techniques. However, IoU analysis showed that CNN based on ResNet technique performed better than FCM for ALO, EVO, and TTO with values of 75.80%, 55.48%, and 77.06%, respectively. Therefore, segmentation through deep learning is more suitable for segmenting the human intestinal parasite ova.

Helminth parasites live in the human body and can cause serious health problems that will lead to cancer and may cause death in patients. These parasitic helminths congregate in the intestines to mate and produce ova. Therefore, early identification screening is necessary to prevent the spread of helminth parasites throughout the body. A manual microscopic feces test is still the most often used approach for helminth detection. As a result, the purpose of this research is to investigate the effectiveness of three classifiers in classifying four types of human intestinal parasite ova. Three classifier techniques used are k-Nearest Neighbourhood (k-NN), Support Vector Machine (SVM), and Ensemble classifier. There are four types of helminth ova which are Ascaris Lumbricoides Ova (ALO), Enterobius Vermicularis Ova (EVO), Hookworm Ova (HWO), and Trichuris Trichiura Ova (TTO). A total of 664 helminth parasite ova images were analyzed, consisting of 166 images from each helminth species. The Linear kernel function from the SVM classifier has obtained the highest accuracy performance reaching 92.23%. Followed by Cityblock distance from the k-NN classifier with an accuracy of 91.16% and AdaBoostM2 from the Ensemble classifier with an accuracy of 89.94%.