Comparison of Human Intestinal Parasite Ova Segmentation Using Machine Learning and Deep Learning Techniques

Chin, Lim Chee; Khairudin, Norhanis Ayunie Ahmad; Loke, Siew Wen; Nasir, Aimi Salihah Abdul; Chong, Yen Fook; Mohamed, Zeehaida

doi:10.3390/app12157542

Cited by 7 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Te high-dimensional GI dataset was reduced by the PCA method [44] and stored in the two feature matrices of size 8000 × 512 for each VGG-16 and DenseNet-121 model. Te SVM classifer receives the endoscopy image feature matrices using VGG-16 [45] and DenseNet-121 [46] models, then fnally trains them at a high speed, and classifes them efciently.…”

Section: Svm Algorithmsmentioning

confidence: 99%

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Al-Mekhlafi

Senan

Alshudukhi

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

Gastrointestinal (GI) diseases, particularly tumours, are considered one of the most widespread and dangerous diseases and thus need timely health care for early detection to reduce deaths. Endoscopy technology is an effective technique for diagnosing GI diseases, thus producing a video containing thousands of frames. However, it is difficult to analyse all the images by a gastroenterologist, and it takes a long time to keep track of all the frames. Thus, artificial intelligence systems provide solutions to this challenge by analysing thousands of images with high speed and effective accuracy. Hence, systems with different methodologies are developed in this work. The first methodology for diagnosing endoscopy images of GI diseases is by using VGG-16 + SVM and DenseNet-121 + SVM. The second methodology for diagnosing endoscopy images of gastrointestinal diseases by artificial neural network (ANN) is based on fused features between VGG-16 and DenseNet-121 before and after high-dimensionality reduction by the principal component analysis (PCA). The third methodology is by ANN and is based on the fused features between VGG-16 and handcrafted features and features fused between DenseNet-121 and the handcrafted features. Herein, handcrafted features combine the features of gray level cooccurrence matrix (GLCM), discrete wavelet transform (DWT), fuzzy colour histogram (FCH), and local binary pattern (LBP) methods. All systems achieved promising results for diagnosing endoscopy images of the gastroenterology data set. The ANN network reached an accuracy, sensitivity, precision, specificity, and an AUC of 98.9%, 98.70%, 98.94%, 99.69%, and 99.51%, respectively, based on fused features of the VGG-16 and the handcrafted.

show abstract

Section: Svm Algorithmsmentioning

confidence: 99%

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Al-Mekhlafi

Senan

Alshudukhi

et al. 2023

International Journal of Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Addressing the issue of complex and time-consuming manual diagnosis of STHs infections, fuzzy c-Mean (FCM) and CNN segmentation technique (ML-and DL-based, respectively) for surveillance of human intestinal parasite ova segmentation were conducted. (Lim et al 2022). Under the direction of parasitologists, a total of 166 pictures for each species were correctly assembled in order to train both ML-based and DL-based segmentation approaches in identifying intestinal STHs ova.…”

Section: Denguementioning

confidence: 99%

Progress and challenges for the application of machine learning for neglected tropical diseases

2023

View full text Add to dashboard Cite

Neglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long existing and have caused devastating health problems and economic decline to people in low- and middle-income (developing) countries. An estimated 1.7 billion of the world’s population suffer one or more NTDs annually, this puts approximately one in five individuals at risk for NTDs. In addition to health and social impact, NTDs inflict significant financial burden to patients, close relatives, and are responsible for billions of dollars lost in revenue from reduced labor productivity in developing countries alone. There is an urgent need to better improve the control and eradication or elimination efforts towards NTDs. This can be achieved by utilizing machine learning tools to better the surveillance, prediction and detection program, and combat NTDs through the discovery of new therapeutics against these pathogens. This review surveys the current application of machine learning tools for NTDs and the challenges to elevate the state-of-the-art of NTDs surveillance, management, and treatment.

show abstract

“…The percentage of minimum value (𝑚𝑖𝑛 𝑝 ) is obtained from the lowest value among the R, G, and B color components out of the total numbers of pixels, likewise for maximum (𝑚𝑎𝑥 𝑝 ). The lowest and highest values obtained must satisfy the conditions in Equation (1) and Equation (2) [16].…”

Section: ) Image Enhancementmentioning

confidence: 99%

Efficient Classification Techniques in Classifying Human Intestinal Parasite Ova

Khairudin,

Lim,

Nasir

et al. 2022

JTEC

View full text Add to dashboard Cite

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%.

show abstract

Comparison of Human Intestinal Parasite Ova Segmentation Using Machine Learning and Deep Learning Techniques

Cited by 7 publications

References 17 publications

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Hybrid Techniques for Diagnosing Endoscopy Images for Early Detection of Gastrointestinal Disease Based on Fusion Features

Progress and challenges for the application of machine learning for neglected tropical diseases

Efficient Classification Techniques in Classifying Human Intestinal Parasite Ova

Contact Info

Product

Resources

About