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05-06-2018 | S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing | Issue 5/2019

Neural Computing and Applications 5/2019

Tuberculosis (TB) detection system using deep neural networks

Journal:
Neural Computing and Applications > Issue 5/2019
Authors:
R. Dinesh Jackson Samuel, B. Rajesh Kanna

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 infectious 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 × 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 support vector machine (SVM) from the transferred knowledge. For training and testing the customized Inception V3 model, a public TB dataset (Shah et al. in J Med Imaging 4(2):027503, 2017. https://​doi.​org/​10.​1117/​1.​jmi.​4.​2.​027503) and our own acquired microscopic digital dataset are used for analysis. In this model, the fixed feature representations are taken from the top stack layer of Inception V3 DeepNet and are classified using SVM. This model attains an accuracy of 95.05%, thereby reducing the dependency on skilled technicians in the screening process and increasing sensitivity and specificity.

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