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Image Sensing Detection in Bio-Systems Using Convolutional Neural Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 4167

Special Issue Editors


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Guest Editor
Wageningen University and Research, Wageningen, The Netherlands
Interests: signal and image processing; optimization; biomedical image analysis; deep learning; robotics in agriculture

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Guest Editor
Institute for Systems and Robotics, Instituto Superior Técnico, 1049-001 Lisboa, Portugal
Interests: machine learning; image analysis; computer vision
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute for Systems and Robotics, Lisbon, Portugal

Special Issue Information

Dear Colleagues,

Introduction: Deep learning is fast emerging as the state-of-the-art approach in many problems in image analysis and computer vision across several scientific domains in the life sciences, including biomedical image analysis and computer vision in agriculture. This Special Issue is focused on solving computer vision problems in bio-systems (e.g., digital phenotyping, agricultural yield estimation, disease detection, and wildlife monitoring) by using convolutional neural networks for object detection. As the field of deep learning is driven by data, the use of these techniques requires area-specific insights. We invite high-quality, previously unpublished contributions on any topics related to the use of deep learning object detection in the aforementioned areas, including (but not limited to) the following:

  • Novel algorithms and convolutional neural network architectures;
  • Innovative use cases in the life sciences;
  • Sensor fusion and multimodality image analysis;
  • Datasets, including synthetic datasets and transfer learning.

Dr. Manya Afonso
Dr. Catarina Barata
Dr. Carlos Santiago
Guest Editors

Manuscript Submission Information

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Keywords

  • deep learning
  • convolutional neural networks
  • object detection
  • bio-systems
  • biomedical image analysis
  • computer vision in life science

Published Papers (1 paper)

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Research

15 pages, 4970 KiB  
Article
Optical Gas Sensing with Liquid Crystal Droplets and Convolutional Neural Networks
by José Frazão, Susana I. C. J. Palma, Henrique M. A. Costa, Cláudia Alves, Ana C. A. Roque and Margarida Silveira
Sensors 2021, 21(8), 2854; https://doi.org/10.3390/s21082854 - 18 Apr 2021
Cited by 13 | Viewed by 3646
Abstract
Liquid crystal (LC)-based materials are promising platforms to develop rapid, miniaturised and low-cost gas sensor devices. In hybrid gel films containing LC droplets, characteristic optical texture variations are observed due to orientational transitions of LC molecules in the presence of distinct volatile organic [...] Read more.
Liquid crystal (LC)-based materials are promising platforms to develop rapid, miniaturised and low-cost gas sensor devices. In hybrid gel films containing LC droplets, characteristic optical texture variations are observed due to orientational transitions of LC molecules in the presence of distinct volatile organic compounds (VOC). Here, we investigate the use of deep convolutional neural networks (CNN) as pattern recognition systems to analyse optical textures dynamics in LC droplets exposed to a set of different VOCs. LC droplets responses to VOCs were video recorded under polarised optical microscopy (POM). CNNs were then used to extract features from the responses and, in separate tasks, to recognise and quantify the vapours exposed to the films. The impact of droplet diameter on the results was also analysed. With our classification models, we show that a single individual droplet can recognise 11 VOCs with small structural and functional differences (F1-score above 93%). The optical texture variation pattern of a droplet also reflects VOC concentration changes, as suggested by applying a regression model to acetone at 0.9–4.0% (v/v) (mean absolute errors below 0.25% (v/v)). The CNN-based methodology is thus a promising approach for VOC sensing using responses from individual LC-droplets. Full article
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