Image-processing based taxonomy analysis of bacterial macromorphology using machine-learning models

Sajedi, Hedieh; Mohammadipanah, Fatemeh; Pashaei, Ali

doi:10.1007/s11042-020-09284-9

Cited by 21 publications

(11 citation statements)

References 24 publications

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“…The feature extraction 4 method is mostly based on texture, shape, color, and other features, and then these features are classified using support vector machines, random forests, and other classifiers. Sajedi et al 5 adopted the Gabor transform method to extract edge features of different directions and intensities of bacterial images for edge detection and pattern recognition. Mao et al 6 proposed a disease classification method based on decision tree and random forest to information such as pupil position and area of eye movement images are extracted as original features.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recognition of honeycomb lung in CT images based on improved MobileNet model

Zhang

Linning

et al. 2021

Medical Physics

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Purpose The research is to improve the efficiency and accuracy of recognition of honeycomb lung in CT images. Methods Deep learning methods are used to achieve automatic recognition of honeycomb lung in CT images, however, are time consuming and less accurate due to the large amount of structural parameters. In this paper, a novel recognition method based on MobileNetV1 network, multiscale feature fusion method (MSFF), and dilated convolution is explored to deal with honeycomb lung in CT image classification. Firstly, the dilated convolution with different dilated rate is used to extract features to obtain receptive fields of different sizes, and then fuse the features of different scales at multiscale feature fusion block is used to solve the problem of feature loss and incomplete feature extraction. After that, by using linear activation functions (Sigmoid) instead of nonlinear activation functions (ReLu) in the improved deep separable convolution blocks to retain the feature information of each channel. Finally, by reducing the number of improved deep separable blocks to reduce the computation and resource consumption of the model. Results The experimental results show that improved MobileNet model has the best performance and the potential for recognition of honeycomb lung image datasets, which includes 6318 images. By comparing with 4 traditional models (SVM, RF, decision tree, and KNN) and 11 deep learning models (LeNet‐5, AlexNet, VGG‐16, GoogleNet, ResNet18, DenseNet121, SENet18, InceptionV3, InceptionV4, Xception, and MobileNetV1), our model achieved the performance with an accuracy of 99.52%, a sensitivity of 99.35%, and a specificity of 99.89%. Conclusion Improved MobileNet model is designed for the automatic recognition and classification of honeycomb lung in CT images. Through experiments comparative analysis of other models of machine learning and deep learning, it is proved that the proposed improved MobileNet method has the best recognition accuracy with fewer the model parameters and less the calculation time.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Recognition of honeycomb lung in CT images based on improved MobileNet model

Zhang

Linning

et al. 2021

Medical Physics

View full text Add to dashboard Cite

show abstract

“…Sajedi et al [20] employed the extreme gradient boosting classification (XGBoost) approach combined with a set of common image-processing methods to classify three different Myxobacterial suborders, i.e., Cystobacterineae, Sorangiineae, and Nannocystineae. The proposed method consisted of two processes: firstly, using the Gabor transform to extract texture features and then applying XGBoost to recognize three categories of bacteria.…”

Section: Related Workmentioning

confidence: 99%

Small-Scale Depthwise Separable Convolutional Neural Networks for Bacteria Classification

Mai

Ishibashi

2021

Electronics

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Bacterial recognition and classification play a vital role in diagnosing disease by determining the presence of large bacteria in the specimens and the symptoms. Artificial intelligence and computer vision widely applied in the medical domain enable improving accuracy and reducing the bacterial recognition and classification time, which aids in making clinical decisions and choosing the proper treatment. This paper aims to provide an approach of 33 bacteria strains’ automated classification from the Digital Images of Bacteria Species (DIBaS) dataset based on small-scale depthwise separable convolutional neural networks. Our five-layer architecture has significant advantages due to the compact model, low computational cost, and reliable recognition accuracy. The experimental results proved that the proposed design reached the highest accuracy of 96.28% with a total of 6600 images and can be executed on limited-resource devices of 3.23 million parameters and 40.02 million multiply–accumulate operations (MACs). The number of parameters in this architecture is seven times less than the smallest model listed in the literature.

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“…al. [ 67 ] used Extreme Gradient Boosting classification (XGBoost) method to classify three different Myxobacterial suborders i.e. Cystobacterineae, Sorangiineae and Nannocystineae .…”

Section: In Bacterial Image Analysismentioning

confidence: 99%

“…al. (2020) [ 67 ] XGBoost Myxobacteria Texture features Myxobacterial dataset – Acc = 90.28% Details of dataset is incomplete More techniques can be applied for better result Abbreviations used in the Tables 3 and 4 —Accuracy (Acc), Classes (C), Total Images (T), Training (Tr), Testing (Te), Sensitivity (Se), Specificity (Sp), Precision (Pre), Recall (Re), Validation (Val) …”

Section: In Bacterial Image Analysismentioning

confidence: 99%

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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Image-processing based taxonomy analysis of bacterial macromorphology using machine-learning models

Cited by 21 publications

References 24 publications

Recognition of honeycomb lung in CT images based on improved MobileNet model

Recognition of honeycomb lung in CT images based on improved MobileNet model

Small-Scale Depthwise Separable Convolutional Neural Networks for Bacteria Classification

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

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