Comparison of Mycobacterium Tuberculosis Image Detection Accuracy
Using CNN and Combination CNN-KNN
Dublin Core
Title
Comparison of Mycobacterium Tuberculosis Image Detection Accuracy
Using CNN and Combination CNN-KNN
Using CNN and Combination CNN-KNN
Subject
mycobacterium tuberculosis, automatic detection system, convolutional neural network, k-nearest neighbor
Description
Mycobacterium tuberculosis is a pathogenic bacterium that causes respiratory tract disease in the lungs, namely tuberculosis
(TB). The problem is to find out the bacterial colonies when the observation is still done manually using a microscope with a
magnification of 1000 times. It took a long time and was tiring for the observer's eye. Based on this background, an automatic
detection system for Mycobacterium tuberculosis was designed. Mycobacterium tuberculosis image data were obtained from
the Semarang City Health Center. The dataset used is 220 sputum images, which are divided into 180 training data and 40
testing data. The method used in this research is a combination of Convolutional Neural Network (CNN) and K-Nearest
Neighbor (KNN). CNN is used for image feature extraction. Furthermore, the results of the CNN feature extraction are
classified using the KNN. The results of the accuracy of the combination of CNN-KNN and CNN were also compared. The
stages of the process are color transformation, feature extraction, and data training with CNN, then classification with KNN.
The results of the classification test between CNN and the CNN-KNN combination show that the CNN-KNN combination is
better. The result of CNN-KNN accuracy is 92.5%, while CNN's accuracy is 90%
(TB). The problem is to find out the bacterial colonies when the observation is still done manually using a microscope with a
magnification of 1000 times. It took a long time and was tiring for the observer's eye. Based on this background, an automatic
detection system for Mycobacterium tuberculosis was designed. Mycobacterium tuberculosis image data were obtained from
the Semarang City Health Center. The dataset used is 220 sputum images, which are divided into 180 training data and 40
testing data. The method used in this research is a combination of Convolutional Neural Network (CNN) and K-Nearest
Neighbor (KNN). CNN is used for image feature extraction. Furthermore, the results of the CNN feature extraction are
classified using the KNN. The results of the accuracy of the combination of CNN-KNN and CNN were also compared. The
stages of the process are color transformation, feature extraction, and data training with CNN, then classification with KNN.
The results of the classification test between CNN and the CNN-KNN combination show that the CNN-KNN combination is
better. The result of CNN-KNN accuracy is 92.5%, while CNN's accuracy is 90%
Creator
Waluyo Nugroho1
, R. Rizal Isnanto2
, Adian Fatchur Rochim3
, R. Rizal Isnanto2
, Adian Fatchur Rochim3
Publisher
Waluyo Nugroho1
, R. Rizal Isnanto2
, Adian Fatchur Rochim3
, R. Rizal Isnanto2
, Adian Fatchur Rochim3
Date
03-02-2023
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
text
Files
Collection
Citation
Waluyo Nugroho1
, R. Rizal Isnanto2
, Adian Fatchur Rochim3, “Comparison of Mycobacterium Tuberculosis Image Detection Accuracy
Using CNN and Combination CNN-KNN,” Repository Horizon University Indonesia, accessed June 30, 2025, https://repository.horizon.ac.id/items/show/9346.
Using CNN and Combination CNN-KNN,” Repository Horizon University Indonesia, accessed June 30, 2025, https://repository.horizon.ac.id/items/show/9346.