Diagnosis of human intestinal parasites by deep learning

Peixinho, Alan Zanoni; Martins, Samuel Botter; Vargas, José A.; Falcão, A. L. Eiras; Gomes, Jancarlo Ferreira; Suzuki, Chikako

doi:10.1201/b19241-19

Cited by 17 publications

(15 citation statements)

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“…Automatic microscopic image analysis performed using DCNN model as a classifier and reported AUC 100% for Malaria and 99% for tuberculosis and hookworm. DCNN also applied for diagnosis of malaria in [23] and intestinal parasites in [20]. Fully CNN Deep learning has been used in [36] for auto-matic cell counting.…”

Section: Gulshan Et Al Applied Deep Convolutional Neural Network (Dcmentioning

confidence: 99%

Deep Learning for Medical Image Processing: Overview, Challenges and the Future

Razzak

Naz²,

Zaib

2017

Lecture Notes in Computational Vision and Biomechanics

917

353

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Healthcare sector is totally different from other industry. It is on high priority sector and people expect highest level of care and services regardless of cost. It did not achieve social expectation even though it consume huge percentage of budget. Mostly the interpretations of medical data is being done by medical expert. In terms of image interpretation by human expert, it is quite limited due to its subjectivity, complexity of the image, extensive variations exist across different interpreters, and fatigue. After the success of deep learning in other real world application, it is also providing exciting solutions with good accuracy for medical imaging and is seen as a key method for future applications in health secotr. In this chapter, we discussed state of the art deep learning architecture and its optimization used for medical image segmentation and classification. In the last section, we have discussed the challenges deep learning based methods for medical imaging and open research issue. M.I. Razzak CPHHI, KSAU-HS, Riyadh

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Section: Gulshan Et Al Applied Deep Convolutional Neural Network (Dcmentioning

confidence: 99%

Deep Learning for Medical Image Processing: Overview, Challenges and the Future

Razzak

Naz²,

Zaib

2017

Lecture Notes in Computational Vision and Biomechanics

917

353

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“…Figures 5 and 6 show that the proposed PC-TEST DOG protocol allowed preparing microscope slides with fecal smears that showed the parasites mostly free of fecal residues, thus improving visualization. This new parasitological protocol freed the parasite structures from these residues ( Figure 6), allowing a more efficient computational image segmentation technique while detecting clean parasite structures [27][28][29]. We concluded that the anionic surfactant SDS that is part of the PC-TEST DOG laboratory protocol acted on the parasite outer membranes, which have indicated to be negatively charged.…”

Section: Discussionmentioning

confidence: 93%

“…In the literature, several works concluded that the larger the image database, the more effective detection computational techniques become [27][28][29][30]. Currently, we have a small image database (10,699 components/data) to train this automated diagnostic system, which should be further extended as the study advances.…”

Section: Discussionmentioning

confidence: 99%

Automated Diagnosis of Canine Gastrointestinal Parasites Using Image Analysis

et al. 2020

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Because canine intestinal parasites are considered cosmopolitan, they carry significant zoonotic potential to public health. These etiological agents are routinely diagnosed using microscopic examination commonly used because of its low cost, simple execution, and direct evidence. However, there are reports in the literature on the poor performance of this test due to low to moderate sensitivity resulting from frequent errors, procedures and interpretation. Therefore, to improve the diagnostic efficiency of microscopic examination in veterinary medicine, we developed and evaluated a unique new protocol. This system was tested in a study involving four genera of highly prevalent canine intestinal parasites in an endemic region in São Paulo state, Brazil. Fecal samples from 104 animals were collected for this research. The new protocol had a significantly higher (p < 0.0001) number of positive cases on image data, including parasites and impurities, and was elaborate to test them with the TF-GII/Dog technique, with a moderate agreement and Kappa index of 0.7636. We concluded that the new Prototic Coproparasitological Test for Dogs (PC-Test Dog) allowed a better visualization of the parasitic structures and showed a favorable result for the diagnosis of intestinal parasites in dogs.

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“…Two last training steps of the CNN model However, in the diagnosis of human intestinal parasites, Peixinho and his colleagues proposed an optical microscopy image analysis approach to discover parasite image features from a small training set (Peixinho et al, 2015). A relevant review of automatic malaria parasites detection was presented by Rosado and his colleague based on microscopic images segmentation.…”

Section: Tablementioning

confidence: 99%

Deep convolution neural network for image recognition

Traoré

Kamsu-Foguem

Tangara

2018

Ecological Informatics

297

110

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During an epidemic crisis, medical image analysis namely microscopic analyses are made to confirm or not the existence of the epidemic pathogen in suspected cases. Pathogen are all infectious agents such as a virus, bacterium, protozoa, prion etc. However, there is often a lack of specialists in the handling of microscopes, hence allowing the need to make the microscopic analysis abroad. This results in a considerable loss of time and in the meantime, the epidemic continues to spread. To save time in the analysis of samples, we propose to make the future microscopes more intelligent so that they will be able to indicate by themselves the existence or not of the pathogen of an epidemic in a sample. To have a smart microscope, we propose a methodology based on efficient Convolution Neural Network (CNN) architecture in order to classify epidemic pathogen with five deep learning phases: (1) Training dataset of provided images (2) CNN Training (3) Testing data preparation (4) CNN generated model on testing data and finally (5) Evaluation of images classified. The resulted classification process can be integrated in a mobile computing solution on future microscopes. CNN can improve the accuracy in pathogens diagnosis that are focused on hand-tuned feature extraction implying some human mistakes. For our study, we consider cholera and malaria epidemics for microscopic images classification with a relevant CNN, respectively Vibrio cholerae images and Plasmodium falciparum images. Image classification is the task of taking an input image and outputting a class or a probability of classes that best describes the image. Interesting results have been obtained from the CNN model generated achieving the classification accuracy of 94%, with 200 Vibrio cholera images and 200 Plasmodium falciparum images for training dataset and 80 images for testing data. Although this document addresses the classification of epidemic pathogen images using a CNN model, the underlying principles apply to the other fields of science and technology, because of its performance and its capability to handle more layers than the previous traditional neural networks.

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Diagnosis of human intestinal parasites by deep learning

Cited by 17 publications

References 0 publications

Deep Learning for Medical Image Processing: Overview, Challenges and the Future

Deep Learning for Medical Image Processing: Overview, Challenges and the Future

Automated Diagnosis of Canine Gastrointestinal Parasites Using Image Analysis

Deep convolution neural network for image recognition

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