Scale-Adaptive Deep Model for Bacterial Raman Spectra Identification

Deng, Lin; Zhong, Yuzhong; Wang, Maoning; Zheng, Xiujuan; Zhang, Jianwei

doi:10.1109/jbhi.2021.3113700

Cited by 24 publications

(36 citation statements)

References 32 publications

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“… 14 Deng et al proposed a method that can learn multi-scale features using the automatic combination of multi-receptive fields of convolutional layers. 15 …”

Section: Introductionmentioning

confidence: 99%

Deeply-recursive convolutional neural network for Raman spectra identification

et al. 2022

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“… 14 Deng et al proposed a method that can learn multi-scale features using the automatic combination of multi-receptive fields of convolutional layers. 15 …”

Section: Introductionmentioning

confidence: 99%

Deeply-recursive convolutional neural network for Raman spectra identification

et al. 2022

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“…The confusion matrix of the eight treatment groups is shown in Figure 2 b. Based on the confusion matrices for this study and other compared models, 12 , 25 it can be seen that there is no significant difference among them, which indicates the superiority of deep learning in this task.…”

Section: Resultsmentioning

confidence: 74%

“…The accuracy is 86.3% in the 30-isolate classifier after testing the model with 3000 spectra. Table 1 shows a comparison of 30-isolates and empiric treatment accuracies in the ResNet, 12 multi-scale, 25 and U-Net model. The confusion matrix of the U-Net model for the 30 isolates is shown in Figure 2 a.…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, this disorder of the distribution is less than that in ResNet and multi-scale models according to their confusion matrices. 12 , 25 …”

Section: Resultsmentioning

confidence: 99%

“…Using the residual neural network (ResNet) with Raman spectroscopy revealed a striking ability to distinguish 30 classes with fair accuracy. In addition, Deng et al 25 have used Stanford Data with a different deep learning model. The study has established a multi-scale 1D CNN model in order to collect more information on the Raman spectra and thereby improve the accuracy of bacterial identification.…”

Section: Introductionmentioning

confidence: 99%

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Highly Accurate Identification of Bacteria’s Antibiotic Resistance Based on Raman Spectroscopy and U-Net Deep Learning Algorithms

et al. 2022

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Bacterial pathogens especially antibiotic-resistant ones are a public health concern worldwide. To oppose the morbidity and mortality associated with them, it is critical to select an appropriate antibiotic by performing a rapid bacterial diagnosis. Using a combination of Raman spectroscopy and deep learning algorithms to identify bacteria is a rapid and reliable method. Nevertheless, due to the loss of information during training a model, some deep learning algorithms suffer from low accuracy. Herein, we modify the U-Net architecture to fit our purpose of classifying the one-dimensional Raman spectra. The proposed U-Net model provides highly accurate identification of the 30 isolates of bacteria and yeast, empiric treatment groups, and antimicrobial resistance, thanks to its capability to concatenate and copy important features from the encoder layers to the decoder layers, thereby decreasing the data loss. The accuracies of the model for the 30-isolate level, empiric treatment level, and antimicrobial resistance level tasks are 86.3, 97.84, and 95%, respectively. The proposed deep learning model has a high potential for not only bacterial identification but also for other diagnostic purposes in the biomedical field.

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Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence

Al‐Shaebi,

Akdeniz,

Ahmed

et al. 2023

Adv Materials Inter

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Antimicrobial resistance (AMR) is a global crisis, responsible for ≈700 000 annual deaths, as reported by the World Health Organization. To counteract this growing threat to public health, innovative solutions for early detection and characterization of drug‐resistant bacterial strains are imperative. Surface‐enhanced Raman spectroscopy (SERS) combined with artificial intelligence (AI) technology presents a promising avenue to address this challenge. This review provides a concise overview of the latest advancements in SERS and AI, showcasing their transformative potential in the context of AMR. It explores the diverse methodologies proposed, highlighting their advantages and limitations. Additionally, the review underscores the significance of SERS in tandem use with machine learning (ML) and deep learning (DL) in combating AMR and emphasizes the importance of ongoing research and development efforts in this critical field. Future developments for this technology could transform the way antimicrobial resistance (AMR) is addressed and pave the way for novel approaches to the protection of public health worldwide.

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Scale-Adaptive Deep Model for Bacterial Raman Spectra Identification

Cited by 24 publications

References 32 publications

Deeply-recursive convolutional neural network for Raman spectra identification

Deeply-recursive convolutional neural network for Raman spectra identification

Highly Accurate Identification of Bacteria’s Antibiotic Resistance Based on Raman Spectroscopy and U-Net Deep Learning Algorithms

Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence

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