Small-Scale Depthwise Separable Convolutional Neural Networks for Bacteria Classification

Mai, Duc-Tho; Ishibashi, Koji

doi:10.3390/electronics10233005

Cited by 11 publications

(10 citation statements)

References 38 publications

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“…Mai et al [20] further investigated the DIBaS dataset and developed a more efficient classification ConvNet, better tailored for utilization on resource-limited devices. The network used depth-wise separable convolutions, which consist of a depth-wise convolution with one convolutional filter for each input channel, followed by a point-wise 1×1 convolution transforming the input to a desired channel depth.…”

Section: Related Workmentioning

confidence: 99%

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy

Hallström

Kandavalli

Elf

et al. 2023

Preprint

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Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For example, the species and its antibiotic susceptibility are essential to treating a bacterial infection. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to discriminate four species of bacteria relevant for human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We evaluate performance of convolutional neural networks and vision transformers, where the best models attain a class-average accuracy of 98%. Our successful proof of principle results suggests that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use.

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Section: Related Workmentioning

confidence: 99%

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy

Hallström

Kandavalli

Elf

et al. 2023

Preprint

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“…In order to create and evaluate a efficient system for classifying bacterial colonies, we used several fine-tuned neural networks trained on Imagenet. We investigated to perform VGG16, ResNet50, MobileNet v1, DS-CNN [11] and DeepBacteria [12] for recogizing 03 bacteria strains in our dataset.…”

Section: Model Architecturementioning

confidence: 99%

“…As a result, 99.25% and 94.85% of the test accuracy were achieved, respectively. Mai et al [11] presented an efficient approach for reliably detecting and classifying related bacterial species in highresolution microscopy images. The proposed method has two key stages.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Classifying Bacteria types using Morphology of Bacterial Colony

Aida¹,

Mai²,

Sun³

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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The significant bottlenecks in determining bacterial species are much more time-consuming and the biology specialist's long-term experience requirements. Specifically, it takes more than half a day to cultivate a bacterium, and then a skilled microbiologist and a costly specialized machine are utilized to analyze the genes and classify the bacterium according to its nucleotide sequence. To overcome these issues as well as get higher recognition accuracy, we proposed applying convolutional neural networks (CNNs) architectures to automatically classify bacterial species based on some key characteristics of bacterial colonies. Our experiment confirmed that the classification of three bacterial colonies could be performed with the highest accuracy (97.19%) using a training set of 5000 augmented images derived from the 40 original photos taken in the Hanoi Medical University laboratory in Vietnam.

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“…The authors in [55] classify bacterial species in the DIBaS dataset. DIBaS is an open-source dataset with 33 classes of bacteria and other microorganisms.…”

Section: Rq 12 Which Types Of Learning Have Been Applied?mentioning

confidence: 99%

A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies

Nagro¹

2022

IJACSA

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Deep learning is an area of machine learning that has substantial potential in various fields of study such as image processing and computer vision. A large number of studies are published annually on deep learning techniques. The focus of this paper is on bacteria detection, identification, and classification. This paper presents a systematic literature review that synthesizes the evidence related to bacteria colony identification and detection published in the year 2021. The aim is to aggregate, analyse, and summarize the evidence related to deep learning detection, identification, and classification of bacteria and bacteria colonies. The significance is that the review will help experts and technicians to understand how deep learning techniques can apply in this regard and potentially further support more accurate detection of bacteria types. A total of 38 studies are analysed. The majority of the published studies focus on supervised-learningbased convolutional neural networks. Furthermore, a large number of studies make use of laboratory-prepared datasets as compared to open-source and industrial datasets. The results also indicate a lack of tools, which is a barrier in adapting academic research in industrial settings.

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Small-Scale Depthwise Separable Convolutional Neural Networks for Bacteria Classification

Cited by 11 publications

References 38 publications

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy

Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy

Deep Learning Approach for Classifying Bacteria types using Morphology of Bacterial Colony

A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies

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