Enhanced $k$-Means Clustering Algorithm for Detection of Human Intestinal Parasites

Nasir, Aimi Salihah Abdul; Khairudin, Norhanis Ayunie Ahmad; Chin, Lim Chee; Aris, Thaqifah Ahmad; Mohamed, Zeehaida

doi:10.1109/iecbes48179.2021.9398801

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An enhanced K-means clustering (EKM) algorithm on the modified global contrast stretching (MGCS) and modified linear contrast stretching (MLCS) enhancement techniques was proposed by [20] to analyze the segmentation performance of unsupervised color-image segmentation of helminth parasites. Moreover, [21] investigated the application of state-of-the-art (SOTA) object detectors for automatically locating and identifying parasite eggs in microscopic images.…”

Section: Related Workmentioning

confidence: 99%

Localization and Classification of Parasitic Eggs in Microscpic Images Using An Efficientdet Detector

AlDahoul

Karim

Kee

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

IPIs caused by protozoan and helminth parasites are among the most common infections in humans in LMICs. They are regarded as a severe public health concern, as they cause a wide array of potentially detrimental health conditions. Researchers have been developing pattern recognition techniques for the automatic identification of parasite eggs in microscopic images. Existing solutions still need improvements to reduce diagnostic errors and generate fast, efficient, and accurate results. Our paper addresses this and proposes a multi-modal learning detector to localize parasitic eggs and categorize them into 11 categories. The experiments were conducted on the novel Chula-ParasiteEgg-11 dataset that was used to train both EfficientDet model with EfficientNet-v2 backbone and EfficientNet-B7+SVM. The dataset has 11,000 microscopic training images from 11 categories. Our results show robust performance with an accuracy of 92%, and an F1 score of 93%. Additionally, the IOU distribution illustrates the high localization capability of the detector.

show abstract

Section: Related Workmentioning

confidence: 99%