Comparison of CNN Architectures for Mycobacterium Tuberculosis Classification in Sputum Images

Tuberculosis (TB) is a preventable and treatable infectious disease, but remains a serious problem in high-risk countries. Accurate early detection remains a challenge despite prevention efforts. The primary method of detecting tuberculosis is identifying bacteria in sputum samples using a microscop...

Full description

Saved in:
Bibliographic Details
Published in:Ingénierie des systèmes d'Information 2024-02, Vol.29 (1), p.49-56
Main Authors: Rachmad, Aeri, Syarief, Mohammad, Hutagalung, Juniar, Hernawati, Suci, Rochman, Eka Mala Sari, Asmara, Yuli Panca
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Artificial neural networks
Bacteria
Classification
Datasets
Deep learning
Disease
Fuzzy logic
Image quality
Image retrieval
Machine learning
Medical imaging
Medical research
Neural networks
Probability
Tuberculosis
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Tuberculosis (TB) is a preventable and treatable infectious disease, but remains a serious problem in high-risk countries. Accurate early detection remains a challenge despite prevention efforts. The primary method of detecting tuberculosis is identifying bacteria in sputum samples using a microscope. This research focuses on the use of Convolutional Neural Network (CNN) with the AlexNet, ResNet-18, ResNet-50, and VGG-16 architectures in the early detection and classification of Tuberculosis (TB) through processing images of TB patients' sputum. A dataset of sputum images was collected and processed to ensure quality and adequate representation. Each CNN model was trained using deep learning techniques on the prepared dataset. The aim of this research is to compare the performance of each model in recognizing and classifying sputum images containing Mycobacterium tuberculosis bacteria and those without TB bacteria. The research results show that AlexNet architecture outperforms ResNet-18, ResNet-50 and VGG-16 in classification accuracy of Mycobacterium tuberculosis. The best validation accuracy achieved was 93.42% with the fastest time of 5 minutes and 52 seconds using AlexNet architecture. Identifying the most appropriate AlexNet architectural model could unlock the potential for developing automated systems that efficiently identify TB, thereby enabling faster and more timely medical intervention.
ISSN:1633-1311
2116-7125
DOI:10.18280/isi.290106