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

© The Institution of Engineering and Technology
Received 05/05/2018, Accepted 26/06/2018, Published 20/07/2018

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.

Inspec keywords: wavelet transforms; data compression; learning (artificial intelligence); image classification; microorganisms; feedforward neural nets; image coding; medical image processing; biomedical optical imaging

Other keywords: wavelet-based compression technology; deep learning approach; digital microbiology imaging; hyperspectral culture plate images; VNIR range; urinary tract infections; full microbiology laboratory automation systems; hyperspectral acquisitions; classification performance degradation; diagnostic images; bacterial growing plates; pathogen identification; robust bacterial species identification; clinical context; spatial-spectral processing; bacterial colonies; convolutional neural network; computed spectral signature classification; lossy data compression; CNN classification; coding strength-driven operating points; strength-driven compression

Subjects: Knowledge engineering techniques; Biology and medical computing; Optical and laser radiation (medical uses); Image recognition; Image and video coding; Neural computing techniques; Computer vision and image processing techniques; Function theory, analysis; Integral transforms; Optical and laser radiation (biomedical imaging/measurement); Data handling techniques; Patient diagnostic methods and instrumentation; Integral transforms

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