Tuberculosis (TB) detection system using deep neural networks

Tuberculosis (TB) detection system using deep neural networks Microscopy is a rapid diagnosis method for many infectious diseases like tuberculosis (TB). In TB bacilli identification, specimens are stained using Ziehl–Neelsen or Auramine dye and are examined by technicians thoroughly for any infec- tious microbes. For pathological study, the images of these microbes are captured using microscopes and image processing is applied for further analysis. However, choosing 100 field of views (FOV) randomly from a 2 9 1 cm square area of sputum specimen may lead to inconsistency in specificity. The examination of specimens is a tedious process, and it requires especially skilled technicians for screening the sputum smear samples. The proposed tuberculosis detection system consists of two subsystems—a data acquisition system and a recognition system. In the data acquisition system, a motorized microscopic stage is designed and developed to automate the acquisition of all FOVs. Here the microscopic stage movement is motorized and scanning patterns are defined by the user for specimen examination. After the acquisition of all FOVs, data are passed to the recognition system. In the recognition system, transfer learning method is implemented by customizing the Inception V3 DeepNet model. This model learns from the pre-trained weights of Inception V3 and classifies the data using http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

Tuberculosis (TB) detection system using deep neural networks

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Publisher
Springer Journals
Copyright
Copyright © 2018 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-018-3564-4
Publisher site
See Article on Publisher Site

Abstract

Microscopy is a rapid diagnosis method for many infectious diseases like tuberculosis (TB). In TB bacilli identification, specimens are stained using Ziehl–Neelsen or Auramine dye and are examined by technicians thoroughly for any infec- tious microbes. For pathological study, the images of these microbes are captured using microscopes and image processing is applied for further analysis. However, choosing 100 field of views (FOV) randomly from a 2 9 1 cm square area of sputum specimen may lead to inconsistency in specificity. The examination of specimens is a tedious process, and it requires especially skilled technicians for screening the sputum smear samples. The proposed tuberculosis detection system consists of two subsystems—a data acquisition system and a recognition system. In the data acquisition system, a motorized microscopic stage is designed and developed to automate the acquisition of all FOVs. Here the microscopic stage movement is motorized and scanning patterns are defined by the user for specimen examination. After the acquisition of all FOVs, data are passed to the recognition system. In the recognition system, transfer learning method is implemented by customizing the Inception V3 DeepNet model. This model learns from the pre-trained weights of Inception V3 and classifies the data using

Journal

Neural Computing and ApplicationsSpringer Journals

Published: Jun 5, 2018

References

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