Combining Deep Convolutional Neural Network with Support Vector Machine to Classify Microscopic Bacteria Images

Ahmed, Tasnim; Wahid, Md. Ferdous; Hasan, Md. Jahid

doi:10.1109/ecace.2019.8679397

Cited by 28 publications

(8 citation statements)

References 12 publications

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“…Therefore, researchers have been developing machine learning techniques to improve or even automate recognition of non-living infectious agents (e.g. viruses [8]) and microorganisms such as algae [9], bacteria [10], fungi [11], and protozoa [12]. However, according to our best knowledge, existing methods focus on identifying a single microbe per microscopy image.…”

Section: Related Workmentioning

confidence: 99%

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning

Borowa

Rymarczyk

Ochońska

et al. 2023

IEEE J. Biomed. Health Inform.

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Preliminary microbiological diagnosis usually relies on microscopic examination and, due to the routine culture and bacteriological examination, lasts up to 11 days. Hence, many deep learning methods based on microscopic images were recently introduced to replace the time-consuming bacteriological examination. They shorten the diagnosis by 1-2 days but still require iterative culture to obtain monoculture samples. In this work, we present a feasibility study for further shortening the diagnosis time by analyzing polyculture images. It is possible with multi-MIL, a novel multi-label classification method based on multiple instance learning.To evaluate our approach, we introduce a dataset containing microscopic images for all combinations of four considered bacteria species. We obtain ROC AUC above 0.9, proving the feasibility of the method and opening the path for future experiments with a larger number of species.

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Section: Related Workmentioning

confidence: 99%

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning

Borowa

Rymarczyk

Ochońska

et al. 2023

IEEE J. Biomed. Health Inform.

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“…The performance of the model tested with five-fold cross-validation was accepted as the average of the obtained values. Ahmed et al [7] and Wahid et al [8] used images with 7 different bacterial species from the Pixnio, Howmed and Microbiology-in-Pictures datasets in their study. In both studies, the inception-v3 network was used as a feature extractor with a transfer learning approach.…”

Section: Related Workmentioning

confidence: 99%

“…f linear (x i , x j )=x i T x j(7) f polynomal (x i , x j )=(αx i T x j +c) d(8) f radial (x i , x j )=exp(-γ|x i -x j | 2 ) (9)…”

mentioning

confidence: 99%

A Hybrid Model: Multiple Feature Selection Approach Using Transfer Learning for Bacteria Classification

Nasip¹,

Zengin²

2022

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Analysis of microscope images is an important topic in medical image processing. However, classification of bacteria, which come in different shapes and sizes and are very small structures, based on their morphological structures is a difficult and time-consuming process that cannot be performed by the naked eye. In this study, a hybrid model for bacterial classification is proposed using transfer learning and feature selection methods together. DenseNet201 is used as a feature extractor with the transfer learning approach in the model. The extracted features were selected separately using four different feature selection algorithms and the best features were merged. The best features were trained and classified using Support Vector Machine (SVM). The dataset used was the Digital Image of Bacterial Species (DIBaS) dataset, which contains 33 bacterial species. The dataset was used with 5-fold and 10-fold cross validation, and the average of the two models was used as the evaluation criterion. In the experimental results, 99.78% accuracy, 99.91% precision, 99.88% sensitivity and 99.89% f-1 score were achieved. Thanks to feature selection, the best features that directly affect the classification performance in the dataset are selected. The proposed model can be helpful in making a preliminary diagnosis or a diagnosis in the clinic. Thanks to its fast and accurate classification performance, it can be used for real-time decision making systems.

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“…As an extension of work [143,1], the Xception-based bacteria classification method is tested in [144]. Xception is pre-trained by ImageNet and then finetuned with experimental data to make it suitable for bacterial classification.…”

Section: Datasetmentioning

confidence: 99%

“…As an extension of works [143], a bacteria classification system based on CNN and SVM is presented in [1]. The data used in this study consists of seven categories collected from several public datasets, including Howmed, Microbiology-in-Pictures, Pixnio and so on.…”

Section: Other Tasksmentioning

confidence: 99%

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Zhang¹,

Li²,

Yin³

et al. 2021

Preprint

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Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The identification, counting, and detection are the basic steps for making full use of different microorganisms. However, the conventional analysis methods are expensive, laborious, and time-consuming. To overcome these limitations, artificial neural networks are applied for microorganism image analysis. We conduct this review to understand the development process of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are introduced. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

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Combining Deep Convolutional Neural Network with Support Vector Machine to Classify Microscopic Bacteria Images

Cited by 28 publications

References 12 publications

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning

A Hybrid Model: Multiple Feature Selection Approach Using Transfer Learning for Bacteria Classification

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

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