Deep learning approach to bacterial colony classification

Zieliński, Bartosz; Plichta, Anna; Misztal, Krzysztof; Spurek, Przemysław; Brzychczy-Włoch, Monika; Ochońska, Dorota

doi:10.1371/journal.pone.0184554

Cited by 112 publications

(68 citation statements)

References 20 publications

(18 reference statements)

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“…For the experiments, we split our DIFaS database (9 strains × 2 preparations × 10 images) into two subsets, so that both of them contain images of all strains, but from different preparation. It is because each preparation has its characteristics, and according to our previous studies [20], using images from the same preparation both in training and test set can result in overstated accuracy. As an example, let us consider the background-size, which depends on the size of the colony moved by inoculation loop from Sabouraud agar to preparation.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

“…Hence, we propose to apply the deep bag-of-words multi-step algorithm shown in Fig 3. In contrast to baseline methods, which utilize "shallow" Neural Network to previously calculated features, our strategies aggregate those features using one of the bag-of-words approaches and then classify them with Support Vector Machine (SVM). Such a policy, previously applied to texture recognition [19] and bacteria colony classification [20], is more accurate than the baseline methods; however, it is not well known. Therefore, to make this paper self-contained, below, we describe its successive steps.…”

Section: Plos Onementioning

confidence: 99%

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Deep learning approach to describe and classify fungi microscopic images

et al. 2020

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Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification of the species due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted therapy may be grave in consequence as the mortality rate for immunosuppressed patients is high. In this paper, we apply a machine learning approach based on deep neural networks and bag-of-words to classify microscopic images of various fungi species. Our approach makes the last stage of biochemical identification redundant, shortening the identification process by 2-3 days, and reducing the cost of the diagnosis.

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Section: Experimental Setup and Resultsmentioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Deep learning approach to describe and classify fungi microscopic images

et al. 2020

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“…In recent years, deep learning algorithms can also be considered as a useful tool for detection and classification of bacteria colonies automatically [24][25][26]. Deep learning is an artificial intelligence algorithm that uses several layers and as layers progress, it extracts higher-level features from a given input.…”

Section: Introductionmentioning

confidence: 99%

Detection of Escherichia Coli Bacteria in Water Using Deep Learning

Yanık

Kaloğlu

Değirmenci

2020

Teh. glas. (Online)

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Considering its importance for vital activities, water and particularly drinking water should be clean and should not contain disease-causing bacteria. One of the pathogenic bacteria found in water is the bacterium Escherichia coli (E. coli). In the commonly used method for the detection of E. coli bacteria, the bacteria samples distilled from the water sample are seeded in endo agar medium and the change in the color of the medium as a result of the metabolic activities of the bacteria is examined with the naked eye. This color change can be seen with the human eye in approximately 22 ± 2 hours. In this study, a new bacteria detection scheme is proposed – using deep learning to detect E. coli bacteria both in shorter time and in practical way. The proposed technique is tested with experimentally collected data. Results show that the detection of bacteria can be done automatically within 6-10 hours with the proposed method.

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“…Recently, an image processing technology using deep learning (DL) and support vector machine (SVM), a machine-learning method, has been dramatically developed. According to several studies, image processing technology has very high classification performance in medical imaging [18–28]. In the ophthalmology field, recent investigations have demonstrated the application of image processing technology involving machine-learning algorithms in medical imaging for various retinal diseases, including BRVO and CRVO, using fundus color photographs and ultra-widefield fundus ophthalmoscopy images [21,23–25,29–32].…”

Section: Introductionmentioning

confidence: 99%

Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning

Nagasato¹,

Tabuchi²,

Masumoto³

et al. 2019

PLoS ONE

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We aimed to assess the ability of deep learning (DL) and support vector machine (SVM) to detect a nonperfusion area (NPA) caused by retinal vein occlusion (RVO) with optical coherence tomography angiography (OCTA) images. The study included 322 OCTA images (normal: 148; NPA owing to RVO: 174 [128 branch RVO images and 46 central RVO images]). Training to construct the DL model using deep convolutional neural network (DNN) algorithms was provided using OCTA images. The SVM used a scikit-learn library with a radial basis function kernel. The area under the curve (AUC), sensitivity and specificity for detecting an NPA were examined. We compared the diagnostic ability (sensitivity, specificity and average required time) between the DNN, SVM and seven ophthalmologists. Heat maps were generated. With regard to the DNN, the mean AUC, sensitivity, specificity and average required time for distinguishing RVO OCTA images with an NPA from normal OCTA images were 0.986, 93.7%, 97.3% and 176.9 s, respectively. With regard to SVM, the mean AUC, sensitivity, and specificity were 0.880, 79.3%, and 81.1%, respectively. With regard to the seven ophthalmologists, the mean AUC, sensitivity, specificity and average required time were 0.962, 90.8%, 89.2%, and 700.6 s, respectively. The DNN focused on the foveal avascular zone and NPA in heat maps. The performance of the DNN was significantly better than that of SVM in all parameters (p < 0.01, all) and that of the ophthalmologists in AUC and specificity (p < 0.01, all). The combination of DL and OCTA images had high accuracy for the detection of an NPA, and it might be useful in clinical practice and retinal screening.

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Deep learning approach to bacterial colony classification

Cited by 112 publications

References 20 publications

Deep learning approach to describe and classify fungi microscopic images

Deep learning approach to describe and classify fungi microscopic images

Detection of Escherichia Coli Bacteria in Water Using Deep Learning

Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning

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