Bacteria Shape Classification using Small-Scale Depthwise Separable CNNs

Mai, Duc-Tho; Ishibashi, Koji

doi:10.1109/embc46164.2021.9630658

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…The results indicate that the proposed biosensing approach for bacterial detection is very effective. In [25], the authors propose an automated deep learning tool to detect and identify three shapes representing three different species of bacteria. The authors make use of depth-wise separable (DS) CNNs for training and classification.…”

Section: ) Cnn-based Architecturesmentioning

confidence: 99%

“…The authors in [53] make use of an annotated dataset of microscopic images to identify bacteria from images. In [25], the authors performed supervised learning as they made use of an annotated dataset composed of microscopic images. The authors in [43] proposed a supervised learning approach for quick identification of bacteria.…”

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

confidence: 99%

“…Open-source datasets publicly available, well known, and have been used in similar studies. For instance, one study used an open-source database called DIBaS, which is freely available and has been used in other similar studies [25].…”

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

confidence: 99%

“…In this case, the dataset was divided into different ratios of training and testing data. The open-source dataset DIBaS was also used by [25] for training and testing. The dataset was reduced to 1,669 relevant microscopic images that were resized to 128x128x3.…”

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

confidence: 99%

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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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Section: ) Cnn-based Architecturesmentioning

confidence: 99%

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

confidence: 99%

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

confidence: 99%

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

confidence: 99%

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

Nagro¹

2022

IJACSA

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“…Deep CNNs were first employed to determine over 20 basic characteristics of bacteria, such as color [5], shape [6], and cell composition, and then combined with a manual classification process. Scientists have improved CNN's ability to perform bacteria classification tasks with a large number of input images in recent years.…”

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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