Diagnosis of Tuberculosis by Using a Fuzzy Logic Expert System

Ngafidin, Khairun Nisa Meiah; Suryono, Suryono; Isnanto, R. Rizal

doi:10.1109/icic47613.2019.8985788

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…For this reason, one of the methods used by doctors on a continuous basis is the detection of tuberculosis by chest x-ray, which provides broader information on the irregularities of lung infections with greater sensitivity [13], but this method has errors in identifying several patients as false positives, identifying them with the bacterium Mycobacterium tuberculosis [14], for this reason, it was necessary to improve this methodology by applying a computerized technique to assist the doctor to detect a possible risk of tuberculosis early and with greater precision.…”

Section: Introductionmentioning

confidence: 99%

Respiratory System Analysis System for Patient Care Against a Possible Risk of Tuberculosis

Brian Meneses-Claudio,

Melissa Yauri-Machaca,

Juan Saberbein-Muñoz

et al. 2024

ARASET

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According to the studies developed in advance, there is a crucial problem of doctors analysing computerised images of the chest visually, making a generalised diagnosis for their patients based on their experience, and making mistakes due to the different characteristics of each patient affected by bacteria in their respiratory tract. An infectious disease that has been increasing over the years is pulmonary tuberculosis, which has had around 12.7 million patients infected in 2020, with low-income countries being the main ones affected by this lung disease that is transmitted from person to person, so it cannot be based on the visual experience of the doctor, as this disease causes an increase of bacteria in the bloodstream and damages the alveoli, although there are various methods of detection, they do not provide a complete result on the patient's condition. The aim of this research is to develop a respiratory tract analysis system that will help doctors to detect tuberculosis earlier and more accurately and avoid prolonged infections that could be fatal for patients. The methodology used for this research is based on carrying out a computer analysis of the patient's chest and then carrying out image processing using MATLAB, using its various digital image processing techniques to detect these conditions. According to the system tests, it was observed that the system performs the detection of tuberculosis with an efficiency of 97.40% in its handling, standing out notoriously for its high value of efficiency, in addition to having the precise time for the determination of tuberculosis in the analysis of computerised images. In conclusion, this system can be used in different circumstances of the patient's condition, from the initial symptoms to an advanced stage of the patient's condition.

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Section: Introductionmentioning

confidence: 99%

Respiratory System Analysis System for Patient Care Against a Possible Risk of Tuberculosis

Brian Meneses-Claudio,

Melissa Yauri-Machaca,

Juan Saberbein-Muñoz

et al. 2024

ARASET

View full text Add to dashboard Cite

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“…Tuberculosis is an infectious disease caused by the bacterium Mycobacterium tuberculosis [1]. This disease can attack the lungs, circulatory system, lymphatic system, and respiratory tract, so if left unchecked it can cause human death [2]. Tuberculosis is caused by a dirty environment and humid areas.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Mycobacterium Tuberculosis Image Detection Accuracy Using CNN and Combination CNN-KNN

Waluyo¹,

Isnanto²,

Rochim³

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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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%.

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“…Deep learning techniques were used to identify tuberculosis disease [13]. A fuzzy expert system was designed for the diagnosis of tuberculosis [14]. Classification and counting of MT from sputum microscopic images were done using fuzzy logic [15].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy ELECTRE and TOPSIS method to analyze the risk factors of tuberculosis

Ezhilarasan

Felix

2022

J. Phys.: Conf. Ser.

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Tuberculosis (TB) is an infectious disease that mainly affects the lungs and it is caused by Mycobacterium tuberculosis bacteria. WHO reported that nearly 1.4 million people died of TB in 2019 and TB is one of the top 10 causes of death in the world. Over 95% of cases and deaths are occurring in developing countries and a lot of factors such as diabetes, immune problem, malnutrition, alcohol, active smoking, crowded places, HIV infection, air pollution, kidney diseases, cancer and tuberculosis treatment workers are linked to TB. So, it is our utmost interest is to analyze the risk factors of TB. Therefore, this present study intended to combine the salient features of ELECTRE and TOPSIS methods under a fuzzy environment to analyze the risk factors of TB. Moreover, the comparative analysis is demonstrated to find the most influencing risk factors of TB. Finally, the sensitivity analysis is also performed using the entropy method to verify the results.

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Diagnosis of Tuberculosis by Using a Fuzzy Logic Expert System

Cited by 6 publications

References 21 publications

Respiratory System Analysis System for Patient Care Against a Possible Risk of Tuberculosis

Respiratory System Analysis System for Patient Care Against a Possible Risk of Tuberculosis

Comparison of Mycobacterium Tuberculosis Image Detection Accuracy Using CNN and Combination CNN-KNN

Fuzzy ELECTRE and TOPSIS method to analyze the risk factors of tuberculosis

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