Bacterial image analysis using multi-task deep learning approaches for clinical microscopy

Background 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. Methods 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. Results 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. Conclusions 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

Synthesis of a nano-silver metal ink for use in thick conductive film fabrication applied on a semiconductor package.

The success of printing technology in the electronics industry primarily depends on the availability of metal printing ink. Various types of commercially available metal ink are widely used in different industries such as the solar cell, radio frequency identification (RFID) and light emitting diode (LED) industries, with limited usage in semiconductor packaging. The use of printed ink in semiconductor IC packaging is limited by several factors such as poor electrical performance and mechanical strength. Poor adhesion of the printed metal track to the epoxy molding compound is another critical factor that has caused a decline in interest in the application of printing technology to the semiconductor industry. In this study, two different groups of adhesion promoters, based on metal and polymer groups, were used to promote adhesion between the printed ink and the epoxy molding substrate. The experimental data show that silver ink with a metal oxide adhesion promoter adheres better than silver ink with a polymer adhesion promoter. This result can be explained by the hydroxyl bonding between the metal oxide promoter and the silane grouping agent on the epoxy substrate, which contributes a greater adhesion strength compared to the polymer adhesion promoter. Hypotheses of the physical and chemical functions of both adhesion promoters are described in detail

FESEM images of two cured samples with different types of adhesion promoters (5.0%) after sintering on a hotplate at 180°C for 1 hour.

<p>FESEM images of two cured samples with different types of adhesion promoters (5.0%) after sintering on a hotplate at 180°C for 1 hour.</p

Schematic of the chemical bonding between the TiO⁻ and the silane coupling agent.

<p>Schematic of the chemical bonding between the TiO⁻ and the silane coupling agent.</p

Figure 1

<p>(a) A printed line pattern on an epoxy molding compound that was printed using silver ink. (b) An FIB cross-sectional view of the thickness of the printed silver layer.</p

Cross-sectional view of the silver film on the epoxy molding compound when no adhesion promoter was used.

<p>Cross-sectional view of the silver film on the epoxy molding compound when no adhesion promoter was used.</p

Abrasion test images.

<p>(a) TiO₂ and (b) PEI inks.</p

Sample groups containing different weight percentages and types of adhesion promoter.

<p>Sample groups containing different weight percentages and types of adhesion promoter.</p

Inter-network of silver particles created from the PEI ink after the heat sintering process.

<p>Inter-network of silver particles created from the PEI ink after the heat sintering process.</p

Chemical structure of the silane coupling agent.

<p>The R-O structure represents the methoxy or ethoxy functional group. The X- structure represents organic coupling groups such as the epoxy, amino or vinyl group.</p