Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection

Najgebauer, Patryk; Grycuk, Rafał; Rutkowski, Leszek; Scherer, Rafał; Siwocha, Agnieszka

doi:10.1007/978-3-030-20912-4_16

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…On the other hand, Górriz et al 31 trained a U-net model for the classification of leishmania parasites into promastigotes, amastigotes and adhered parasites. Najgebauer et al 32 proposed a technique that uses a fully convolutional network (FCN) to analyze the complete sample space and give a class to each pixel in the image. The program was taught to identify parasite eggs and differentiate them from the adjacent or overlapping pollution.…”

Section: Previous Workmentioning

confidence: 99%

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%

Parasitic egg recognition using convolution and attention network

AlDahoul,

Karim,

Momo

et al. 2023

Sci Rep

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“…Many of the recent studies have used deep learning for segmentation [ 39 ] where like this inspiration, the proposed study also used DL for segmentation purpose. Similar to this, DL used in various recognition and classification tasks now-a-days [ 40 ].…”

Section: Methodsmentioning

confidence: 99%

“…Te labeling images with nodule and background class are created which is later given to CNN with input images as input data. [39] where like this inspiration, the proposed study also used DL for segmentation purpose. Similar to this, DL used in various recognition and classifcation tasks now-a-days [40].…”

Section: Methodsmentioning

confidence: 99%

Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection

JAVED

Liaqat

Meraj

et al. 2023

Computational Intelligence and Neuroscience

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Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient’s survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer early is a complicated task due to having the visual similarity of lungs nodules with trachea, vessels, and other surrounding tissues that leads toward misclassification of lung nodules. Therefore, correct identification and classification of nodules is required. Previous studies have used noisy features, which makes results comprising. A predictive model has been proposed to accurately detect and classify the lung nodules to address this problem. In the proposed framework, at first, the semantic segmentation was performed to identify the nodules in images in the Lungs image database consortium (LIDC) dataset. Optimal features for classification include histogram oriented gradients (HOGs), local binary patterns (LBPs), and geometric features are extracted after segmentation of nodules. The results shown that support vector machines performed better in identifying the nodules than other classifiers, achieving the highest accuracy of 97.8% with sensitivity of 100%, specificity of 93%, and false positive rate of 6.7%.

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“…Later, a transfer learning strategy using a pretrained AlexNet [8] and a pretrained ResNet50 [9] was employed to classify parasitic egg types, and the positions of the eggs were identified using sliding window technique [2]. Some methods follow semantic segmentation using UNet architecture [10] or a fully convolutional network (FCN) [11]. These techniques however require full pixel-wise annotation.…”

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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Microscopic Sample Segmentation by Fully Convolutional Network for Parasite Detection

Cited by 6 publications

References 13 publications

Parasitic egg recognition using convolution and attention network

Parasitic egg recognition using convolution and attention network

Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection

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

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