Abstract

Manual bacilli detection from Zeihl-Neelsen (ZN) stain images is tedious results in error due to bacteria size and lack of trained experts. Bacilli detection is often a complex task due to their numbers and stain particles. Automatic detection models are the best solution to increase the accuracy of bacilli detection. In the proposed work bacilli detection model using Deep Convolution Neural Network (CNN) is proposed. Preprocessing and segmentation are also explored in the present study. A model such as VGG16, ResNet50, and SqueezeNet are explored. A comparison study is carried to analyze the performance metrics. A proposed model using SqueezeNet as a classifier gives an overall accuracy of 97%.

Original languageEnglish
Title of host publicationProceedings of the 2021 2nd International Conference on Communication, Computing and Industry 4.0, C2I4 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665420136
DOIs
Publication statusPublished - 2021
Event2nd International Conference on Communication, Computing and Industry 4.0, C2I4 2021 - Bangalore, India
Duration: 16-12-202117-12-2021

Publication series

NameProceedings of the 2021 2nd International Conference on Communication, Computing and Industry 4.0, C2I4 2021

Conference

Conference2nd International Conference on Communication, Computing and Industry 4.0, C2I4 2021
Country/TerritoryIndia
CityBangalore
Period16-12-2117-12-21

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Industrial and Manufacturing Engineering
  • Instrumentation

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