Realtime Optical Acquisition And Classification System For Microbiology Applications

• doi: 10.1109/MELECON56669.2024.10608595
• title: Real-time optical acquisition and classification system for microbiology applications
• publisher: IEEE
• isbn: 979-8-3503-8703-2
• issn: 2158-8473
• rank: 3415
• access_type: LOCKED
• content_type: Conferences
• abstract: This paper presents an image-based real-time microorganism classification system using deep learning object detection methods, namely YOLOv5. This prototype was developed using an optical kit consisting of a high amplification optical tube, as well as high sensitivity and spectral domain CMOS sensors, coupled to a dedicated deep learning application, a Nvidia Jetson Nano computer, running the trained state-of-the-art YOLOv5 architecture. For this purpose, an image dataset was assembled using 1193 images of the Escherichia coli (E. coli) bacteria taken from traditional microscopy and a digital camera. Each image was thoroughly labelled. Image data augmentation was also used to extend data. The classification system showed promising results, indicating that there is potential for further development of the model and setup. The proposed prototype lays a solid foundation for improving classification results in portable applications without the need for traditional but time consuming laboratory preparations for the analysis and identification of microorganisms.
• article_number: 10608595
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10608595
• html_url: https://ieeexplore.ieee.org/document/10608595/
• abstract_url: https://ieeexplore.ieee.org/document/10608595/
• publication_title: 2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)
• conference_location: Porto, Portugal
• conference_dates: 25-27 June 2024
• publication_number: 10608458
• is_number: 10608460
• publication_year: 2024
• publication_date: 25-27 June 2024
• start_page: 711
• end_page: 716
• citing_paper_count: 0
• citing_patent_count: 0
• download_count: 39
• insert_date: 20240730
• index_terms:
  • ieee_terms:
    • YOLO
    • Deep learning
    • Stimulated emission
    • Prototypes
    • Optical imaging
    • Solids
    • Real-time systems
  • author_terms:
    • Deep learning
    • YOLO
    • image-based classification
    • microbiology
  • dynamic_index_terms:
    • Real-Time System
    • Optical System
    • Optical Devices
    • Deep Learning
    • Digital Camera
    • Classification Results
    • Image Dataset
    • Object Detection
    • Traditional Microscopy
    • Laboratory Preparation
    • Neural Network
    • Convolutional Neural Network
    • Deep Neural Network
    • Image Classification
    • Automatic System
    • Deep Learning Models
    • Dataset Size
    • Confusion Matrix
    • Confusion Matrices
    • Bounding Box
    • Object Classification
    • Optical Sensors
    • Solid-state Drives
    • SSDs
    • Coli Bacteria
    • Still Images
    • Validation Loss
    • Real Class
    • Progressive Training
    • Weight Training
    • Legionella
    • Legionella Pneumophila
    • Image Sensor
    • Camera Sensor
• isbn_formats:
  • format: Print on Demand(PoD) ISBN, value: 979-8-3503-8703-2, isbnType: New-2005
  • format: Electronic ISBN, value: 979-8-3503-8702-5, isbnType: New-2005
• authors:
  • Author Name: Telmo Marques
    Affiliation: School of Technology of Tomar, ESTT Polytechnic Institute of Tomar, Tomar, Portugal
    Author URL: https://ieeexplore.ieee.org/author/609053455200377
    ID: 609053455200377
    Order: 1
    Author Affiliations:
    • School of Technology of Tomar, ESTT Polytechnic Institute of Tomar, Tomar, Portugal
  • Author Name: Pedro Correia
    Affiliation: Ci2, Smart Cities Research Center Polytechnic Institute of Tomar, Tomar, Portugal
    Author URL: https://ieeexplore.ieee.org/author/37408245400
    ID: 37408245400
    Order: 2
    Author Affiliations:
    • Ci2, Smart Cities Research Center Polytechnic Institute of Tomar, Tomar, Portugal
  • Author Name: Manuel Barros
    Affiliation: Ci2, Smart Cities Research Center Polytechnic Institute of Tomar, Tomar, Portugal
    Author URL: https://ieeexplore.ieee.org/author/37269369400
    ID: 37269369400
    Order: 3
    Author Affiliations:
    • Ci2, Smart Cities Research Center Polytechnic Institute of Tomar, Tomar, Portugal
  • Author Name: Henrique Pinho
    Affiliation: Ci2, Smart Cities Research Center Polytechnic Institute of Tomar, Tomar, Portugal
    Author URL: https://ieeexplore.ieee.org/author/886200733648545
    ID: 886200733648545
    Order: 4
    Author Affiliations:
    • Ci2, Smart Cities Research Center Polytechnic Institute of Tomar, Tomar, Portugal
  • Author Name: Dina Mateus
    Affiliation: Techn&Art Research Centre Polytechnic Institute of Tomar, Tomar, Portugal
    Author URL: https://ieeexplore.ieee.org/author/172072020389185
    ID: 172072020389185
    Order: 5
    Author Affiliations:
    • Techn&Art Research Centre Polytechnic Institute of Tomar, Tomar, Portugal
  • Author Name: Rui Goncalves
    Affiliation: Ci2, Smart Cities Research Center Polytechnic Institute of Tomar, Tomar, Portugal
    Author URL: https://ieeexplore.ieee.org/author/795542510469552
    ID: 795542510469552
    Order: 6
    Author Affiliations:
    • Ci2, Smart Cities Research Center Polytechnic Institute of Tomar, Tomar, Portugal

Image Sensor

• sensor_type: CMOS
• resolution: 4056 (H) x 3040 (V)
• dynamic_range: Not specified
• pixel_size: 1.55 µm
• dark_current: Not specified

Optical Data

• focal_length: Not specified
• aperture: Not specified
• field_of_view: Not specified
• distortion: Not specified

Performance Metrics

• frame_rate: 240 frames per second

Applications & Benefits

• cell_imaging: Used in the detection and classification of microorganisms in microbiology applications.
• benefits: Enables real-time image capture and classification of microorganisms, reducing the need for traditional lab work.

Supporting Organizations

• supported_by: Portuguese Foundation for Science and Technology (FCT)

Manuscript Details

• publication_date: 25-27 June 2024

Relevancy Score

• score: 9
• missing_fields:
  • dynamic_range
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • signal_to_noise_ratio
  • sensitivity
  • shutter_speed
  • power_consumption
  • noise

Processed JSON Filename

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