人工智能
机器学习
深度学习
算法
抗生素耐药性
鉴定(生物学)
计算机科学
人工神经网络
自编码
抗生素
微生物学
生物
植物
作者
Zakarya Al‐Shaebi,Fatma Uysal Ciloglu,Mohammed Nasser,Ömer Aydın
出处
期刊:ACS omega
[American Chemical Society]
日期:2022-08-12
卷期号:7 (33): 29443-29451
被引量:14
标识
DOI:10.1021/acsomega.2c03856
摘要
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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