Bacterial classification with convolutional neural networks based on different data reduction layers

Abd-Alhalem, Samia M.; Soliman, Naglaa F.; Eldin, Salah; Elrahman, S E Abd; Ismail, Nabil A.; El-Rabaie, El-Sayed M.; El‐Samie, Fathi E. Abd

doi:10.1080/15257770.2019.1645851

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…To address these difficulties various image pre-processing techniques like adaptive median filtering and Gaussian filtering are employed. In addition to this the researchers have several other opportunities to explore more in image pre-processing techniques like Wiener filter, unsharp mask filtering, deep neural networks such as autoencoders [ 70 ], deep residual dense network [ 21 ], CNN [ 62 ], linear contrast adjustment, etc. More feature descriptors using the combination of colour and texture features can also be applied for extracting more significant features from bacterial images [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, both the quality and quantity of data are insufficient for creating effective ML model. Some researchers ([ 45 , 62 , 64 , 65 ]) merge one ML technique with other ML techniques, like SVM with radial basis function, CNN with random projection and DL with LSTM, ResNet and 1D-CNN to produce hybrid model for bacterial image classification. However the desired efficiency of the model is yet to be attained due to poor data quality.…”

Section: Discussionmentioning

confidence: 99%

“…Abd-Alhalem et al [ 62 ] presented DL technique for classification of bacteria like ( Actinobacteria, Firmicutes and Proteobacteriaon ) on the basis of DNA nucleotides i.e. A, T, C & G using CNN based on random projection on different data reduction layers.…”

Section: In Bacterial Image Analysismentioning

confidence: 99%

See 2 more Smart Citations

Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues

Kotwal

Rani

Arif

et al. 2021

Arch Computat Methods Eng

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Bacteria are important in a variety of practical domains, including industry, agriculture, medicine etc. A very few species of bacteria are favourable to humans. Whereas, majority of them are extremely dangerous and causes variety of life threatening illness to different living organisms. Traditionally, this class of microbes is detected and classified using different approaches like gram staining, biochemical testing, motility testing etc. However with the availability of large amount of data and technical advances in the field of medical and computer science, the machine learning methods have been widely used and have shown tremendous performance in automatic detection of bacteria. The inclusion of latest technology employing different Artificial Intelligence techniques are greatly assisting microbiologist in solving extremely complex problems in this domain. This paper presents a review of the literature on various machine learning approaches that have been used to classify bacteria, for the period 1998–2020. The resources include research papers and book chapters from different publishers of national and international repute such as Elsevier, Springer, IEEE, PLOS, etc. The study carried out a detailed and critical analysis of penetrating different Machine learning methodologies in the field of bacterial classification along with their limitations and future scope. In addition, different opportunities and challenges in implementing these techniques in the concerned field are also presented to provide a deep insight to the researchers working in this field.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues

Kotwal

Rani

Arif

et al. 2021

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…In the CNN model, convolutional layer neurons are able to extract higher-level abstraction features from features extracted at the previous layer. CNN was applied with success in DNA studies [ [43] , [44] , [45] , [46] ], Breast Cancer Cell Segmentation [ 47 , 48 ], medical diagnosis [ 49 , 50 ], character recognition [ 51 ] and in other areas of application.…”

Section: Methodsmentioning

confidence: 99%

New methodology for repetitive sequences identification in human X and Y chromosomes

Touati

Tajouri

Mesaoudi

et al. 2021

Biomedical Signal Processing and Control

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“…Despite several research works focusing on automating the classification of various bacterial species [5][6][7][8][9] , the inconsistency in image acquisition tools (e.g., hyperspectral imaging or digital microscopic imaging) and image segmentation techniques make the model difficult to be generalized for other studies or for commercial use. Additionally, the majority of research conducted utilized imbalanced datasets and limited performance evaluation metrics, for instance, only accuracy was used as an sole index for assessing the model validity 5 .…”

mentioning

confidence: 99%

Automated Microbial Classification System based on Deep Convolutional Neural Networks using Images from Colony Picker

Park,

Yeoh,

Choo

et al. 2023

Preprint

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Colony screening in single and multi-species environments is an essential step for microbiome studies. However, it possesses a high possibility of inaccurately classifying the species of interest and demands a high degree of manpower and time. Thus, automating the classification of microbes is beneficial to minimize the time and inaccuracy in the colony screening/picking step. Here, we developed an automated microbial classification system for five target species, based on deep Convolutional Neural Networks (CNN) using images captured by an automated robotic colony picker. Multiple possible scenarios of colony culture and diverse morphologies of colonies were examined in building the training and test datasets to train and validate the model and performance on real-life implementations. The final model trained using 60,000 training images, with 12,000 images per species and 3-fold cross-validation, achieved a test accuracy of 94.2% and a test loss of 0.154. Upon testing using a deployment dataset of 4,500 images (900 images per species) with different methods of applying cells onto the agar plate, high accuracy of up to 96.6% was obtained. Five evaluation metrics were implemented to evaluate diverse scenarios of the test data to justify the validity of the model in real-life applications. This model forms a foundation for the classification of more species through transfer learning in the future.

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Bacterial classification with convolutional neural networks based on different data reduction layers

Cited by 8 publications

References 14 publications

Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues

Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues

New methodology for repetitive sequences identification in human X and Y chromosomes

Automated Microbial Classification System based on Deep Convolutional Neural Networks using Images from Colony Picker

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