NDLI: Combining the use of CNN classification and strength-driven compression for the robust identification of bacterial species on hyperspectral culture plate images

Content Provider	IET Digital Library
Author	Signoroni, Alberto Savardi, Mattia Pezzoni, Mario Guerrini, Fabrizio Arrigoni, Simone Turra, Giovanni
Abstract	Huge streams of diagnostic images are expected to be produced daily in the emerging field of digital microbiology imaging because of the ongoing worldwide spread of Full Laboratory Automation systems. This is redefining the way microbiologists execute diagnostic tasks. In this context, the authors want to assess the suitability and effectiveness of a deep learning approach to solve the diagnostically relevant but visually challenging task of directly identifying pathogens on bacterial growing plates. In particular, starting from hyperspectral acquisitions in the VNIR range and spatial-spectral processing of cultured plates, they approach the identification problem as the classification of computed spectral signatures of the bacterial colonies. In a highly relevant clinical context (urinary tract infections) and on a database of acquired hyperspectral images, they designed and trained a convolutional neural network for pathogen identification, assessing its performance and comparing it against conventional classification solutions. At the same time, given the expected data flow and possible conservation and transmission needs, they are interested in evaluating the combined use of classification and lossy data compression. To this end, after selecting a suitable wavelet-based compression technology, they test coding strength-driven operating points looking for configurations able to provably prevent any classification performance degradation.
Starting Page	941
Ending Page	949
Page Count	9
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 7, Oct (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5237
Journal	IET Computer Vision
Publisher Date	2018-07-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Bacterial Colonies Bacterial Growing Plates Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging Classification Performance Degradation Clinical Context CNN Classification Coding Strength-driven Operating Points Computed Spectral Signature Classification Computer Vision And Image Processing Technique Convolutional Neural Network Data Compression Data Handling Technique Deep Learning Approach Diagnostic Image Digital Microbiology Imaging Feedforward Neural Network Full Microbiology Laboratory Automation System Function Theory And Analysis Hyperspectral Acquisitions Hyperspectral Culture Plate Image Image And Video Coding Image Classification Image Coding Image Recognition Integral Transforms Knowledge Engineering Technique Learning in AI Lossy Data Compression Medical Image Processing Microorganisms Neural Computing Technique Optical And Laser Radiation Pathogen Identification Patient Diagnostic Method And Instrumentation Robust Bacterial Species Identification Spatial-spectral Processing Strength-driven Compression Urinary Tract Infections VNIR Range Wavelet Transform Wavelet-based Compression Technology
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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