Detection and Identification of Parasite Eggs from Microscopic Images of Fecal Samples

Ray, Kaushik; Shil, Sukhen; Saharia, Sarat; Sarma, Nityananda; Karabasanavar, Nagappa S.

doi:10.1007/978-981-13-9042-5_5

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Preprocessing involved median filtering, binary thresholding, segmentation, and feature (size, shape, eggshell) extraction. While 12 , in a recent study, classified Ascaris eggs using ANN coupled with Multi-class Support Vector Machine (MC-SVM). The experimental findings indicated a 95% and 93% accuracy rate in identifying Ascaris eggs, respectively.…”

Section: Previous Workmentioning

confidence: 99%

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Parasitic egg recognition using convolution and attention network

AlDahoul,

Karim,

Momo

et al. 2023

Sci Rep

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Intestinal parasitic infections (IPIs) caused by protozoan and helminth parasites are among the most common infections in humans in low-and-middle-income countries. IPIs affect not only the health status of a country, but also the economic sector. Over the last decade, pattern recognition and image processing techniques have been developed to automatically identify parasitic eggs in microscopic images. Existing identification techniques are still suffering from diagnosis errors and low sensitivity. Therefore, more accurate and faster solution is still required to recognize parasitic eggs and classify them into several categories. A novel Chula-ParasiteEgg dataset including 11,000 microscopic images proposed in ICIP2022 was utilized to train various methods such as convolutional neural network (CNN) based models and convolution and attention (CoAtNet) based models. The experiments conducted show high recognition performance of the proposed CoAtNet that was tuned with microscopic images of parasitic eggs. The CoAtNet produced an average accuracy of 93%, and an average F1 score of 93%. The finding opens door to integrate the proposed solution in automated parasitological diagnosis.

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Section: Previous Workmentioning

confidence: 99%

“…Many studies have implemented systems to analyze the micrographs of the samples based on machine learning, e.g. support vector machine (SVM) 12 and artificial neural networks (ANN) 12 , 13 . These traditional machine learning methods do not need complex structures, but they rely largely on selectively selected information.…”

Section: Introductionmentioning

confidence: 99%

Parasitic egg recognition using convolution and attention network

AlDahoul,

Karim,

Momo

et al. 2023

Sci Rep

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“…Existing detection and classification methods: Traditional methods of parasite egg detection involve pre-processing, feature extraction and classification with support vector machine (SVM) [4,5] or artificial neural networks (ANN) [4,6]. The performances are however limited because the methods cannot deal with complex datasets.…”

Section: Related Workmentioning

confidence: 99%

ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results

Anantrasirichai

Chalidabhongse

Palasuwan

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Manual examination of faecal smear samples to identify the existence of parasitic eggs is very time-consuming and can only be done by specialists. Therefore, an automated system is required to tackle this problem since it can relate to serious intestinal parasitic infections. This paper reviews the ICIP 2022 Challenge on parasitic egg detection and classification in microscopic images. We describe a new dataset for this application, which is the largest dataset of its kind. The methods used by participants in the challenge are summarised and discussed along with their results.

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“…Identification or detection is the final step to find out the object based on its characteristics [22]. This process can be done by conducting training and testing [23] [24].…”

Section: Identification (Detection)mentioning

confidence: 99%

K-Means Clustering for Egg Embryo's Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image

Saifullah

2020

Sinergi

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This research discusses the detection of embryonic eggs using the k-means clustering method based on statistical feature extraction. The processes that occur in detection are image acquisition, image enhancement, feature extraction, and identification/detection. The data used consisted of 200 egg image data, consisting of 100 test data and 100 new test data. The acquisition process uses a smartphone camera by capturing candled egg objects. The results of image acquisition become a reference in the process of image enhancement and feature extraction using Statistical Feature Extraction. The statistical feature extraction applied is the Gray Level Co-occurrence Matrix (GLCM) method, which consists of 6 features, namely Energy, Contrast, Entropy, Variance, Correlation, and Homogeneity. The results of feature extraction (6 features) are grouped by the K-means Clustering method. The clustering process uses Euclidean distance calculations to determine the proximity of features. The results of grouping and testing give the best average results with an accuracy of ≈ 74% from several test samples.

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Detection and Identification of Parasite Eggs from Microscopic Images of Fecal Samples

Cited by 8 publications

References 13 publications

Parasitic egg recognition using convolution and attention network

Parasitic egg recognition using convolution and attention network

ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results

K-Means Clustering for Egg Embryo's Detection Based-on Statistical Feature Extraction Approach of Candling Eggs Image

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