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
Neural Computing and Applications – Springer Journals
Published: Jun 5, 2018
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