■细菌图像分析在各个领域中起着至关重要的作用,为研究细菌结构生物学提供有价值的信息和见解,诊断和治疗由病原菌引起的传染病,发现和开发可以对抗细菌感染的药物,等。因此,它促使人们努力自动化细菌图像分析任务。通过自动化分析任务和利用更先进的计算技术,例如深度学习(DL)算法,细菌图像分析有助于快速,更准确,高效,可靠,和标准化分析,导致增进理解,诊断,和控制细菌相关的现象。
■DL算法的三个目标检测网络,即SSD-MobileNetV2,EfficientDet,和YOLOv4,被开发用于自动检测大肠杆菌(E.大肠杆菌)显微镜图像中的细菌。开发了多任务DL框架,以根据其各自的生长阶段对细菌进行分类,其中包括杆状细胞,分裂的细胞,和微菌落。在训练目标检测模型之前进行数据预处理步骤,包括图像增强,图像注释,和数据拆分。使用基于平均精度(MAP)的定量评估方法评估DL技术的性能,精度,召回,和F1得分。对模型的性能指标进行了比较和分析。然后选择最佳的DL模型来执行识别杆状细胞的多任务对象检测,分裂的细胞,和微菌落。
■从三个建议的DL模型生成的测试图像的输出显示出较高的检测精度,YOLOv4实现检测的最高置信度得分范围,并能够为大肠杆菌细菌的不同生长阶段创建不同颜色的边界框。在统计分析方面,在提出的三个模型中,YOLOv4表现出卓越的性能,以最高的精度实现98%的最高MAP,召回,F1得分为86%,97%,91%,分别。
■这项研究证明了其有效性,潜力,以及DL方法在多任务细菌图像分析中的适用性,专注于从显微图像中自动化细菌的检测和分类。所提出的模型可以输出带有包围每个检测到的大肠杆菌细菌的边界框的图像,标记为它们的生长阶段和检测的置信水平。所有提出的目标检测模型都取得了有希望的结果,YOLOv4优于其他型号。
UNASSIGNED: Bacterial image analysis plays a vital role in various fields, providing valuable information and insights for studying bacterial structural biology, diagnosing and treating infectious diseases caused by pathogenic bacteria, discovering and developing drugs that can combat bacterial infections, etc. As a result, it has prompted efforts to automate bacterial image analysis tasks. By automating analysis tasks and leveraging more advanced computational techniques, such as deep learning (DL) algorithms, bacterial image analysis can contribute to rapid, more accurate, efficient, reliable, and standardised analysis, leading to enhanced understanding, diagnosis, and control of bacterial-related phenomena.
UNASSIGNED: Three object detection networks of DL algorithms, namely SSD-MobileNetV2, EfficientDet, and YOLOv4, were developed to automatically detect Escherichia coli (E. coli) bacteria from microscopic images. The multi-task DL framework is developed to classify the bacteria according to their respective growth stages, which include rod-shaped cells, dividing cells, and microcolonies. Data preprocessing steps were carried out before training the object detection models, including image augmentation, image annotation, and data splitting. The performance of the DL techniques is evaluated using the quantitative assessment method based on mean average precision (mAP), precision, recall, and F1-score. The performance metrics of the models were compared and analysed. The best DL model was then selected to perform multi-task object detections in identifying rod-shaped cells, dividing cells, and microcolonies.
UNASSIGNED: The output of the test images generated from the three proposed DL models displayed high detection accuracy, with YOLOv4 achieving the highest confidence score range of detection and being able to create different coloured bounding boxes for different growth stages of E. coli bacteria. In terms of statistical analysis, among the three proposed models, YOLOv4 demonstrates superior performance, achieving the highest mAP of 98% with the highest precision, recall, and F1-score of 86%, 97%, and 91%, respectively.
UNASSIGNED: This study has demonstrated the effectiveness, potential, and applicability of DL approaches in multi-task bacterial image analysis, focusing on automating the detection and classification of bacteria from microscopic images. The proposed models can output images with bounding boxes surrounding each detected E. coli bacteria, labelled with their growth stage and confidence level of detection. All proposed object detection models have achieved promising results, with YOLOv4 outperforming the other models.