Intelligent System for Diagnosing Tuberculosis Using Adaptive Neuro-Fuzzy

Goni, Ibrahim; Ngene, Christopher Umerah; Manga, I; Nata’ala, Auwal; Calvin, Sunday J.

doi:10.9734/ajrcos/2018/v2i124763

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…In Xiong et al [24], study The 47 inconsistent cases were reviewed manually by pathologists both via microscope and digital slides. In Goni et al [25], study physical symptoms-based attributes were only used with a limited data sample size. In the study of Ramandeep Singh et al, it was aimed to assess the accuracy of the deep learning algorithm for the detection of abnormalities on routine frontal chest radiographs only.…”

Section: Resultsmentioning

confidence: 99%

An Analysis of Adaptable Intelligent Models for Pulmonary Tuberculosis Detection and Classification

Siddiqui

Garg

2021

SN COMPUT. SCI.

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Section: Resultsmentioning

confidence: 99%

An Analysis of Adaptable Intelligent Models for Pulmonary Tuberculosis Detection and Classification

Siddiqui

Garg

2021

SN COMPUT. SCI.

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“…The evaluation of the system was done by using the Trapezoidal membership function and backpropagation algorithm. The model was efficient in learning in a shorter time and gave good results [108].…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 97%

Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

et al. 2022

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Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to the artificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, and artificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis.

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“…[26] Used adaptive Neurofuzzy inference system to diagnosed Ebola. [27] Used adaptive Neuro-fuzzy system to create intelligent system for diagnosing tuberculosis. [28] Applied Neuro-fuzzy model to predict the presence of mycobacterium TB.…”

Section: Introductionmentioning

confidence: 99%

Image Processing Approach to Determine the Severity Level of Tuberculosis

Auwal

Goni

Ali

et al. 2019

CJAST

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Tuberculosis (TB) is a disease caused by a bacterium called Mycobacterium tuberculosis. Basically there are two types of TB, latent and pulmonary TB. It symptoms include persistent cough for more than two weeks, cough with blood, night sweat, tiredness, fever, chest pain, lymph node enlargement. It treatment depends on severity level such as mild, moderate, severe and very severe and this can be obtained from TB diagnosis such as chest x-ray image, nucleic acid amplification test and culture and sensitivity. The main aim of this work is to determine severity level of TB using image processing techniques. Chest x-ray (CXR) images of TB patients were obtained from Google image. Firstly, the CXR images were enhanced from Red Green Blue (RGB) to gray image (GI) form. Secondly, GI were decomposed, convolved, compressed and filtered these processing is called image degradation. Thirdly, These GI were restored and binarized. Threshold value ≥ 53 was obtained to classify the severity level of the infected image Furthermore, average error was calculated and system performance obtained. This work revealed that image 1, 2, 6 and 11 has a total pixel value 11067, 6735, 9256 and 3894 respectively ≥ 53 which indicates that the % of the infected areas as 99.62%, 99.52%, 96.70% and 82.78% respectively which shows the severity level are very severe. While the image 3, 4, 5, 8 and 9 has a percentage of infected areas 73.13%, 72.90%, 76.80%, and 76.84% respectively which indicate the severity level as severe. Image 7 has a percentage of infected area 51.21 which shows the severity level as moderate. This research work revealed that image processing is a suitable technique for determine the severity level of TB.

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Intelligent System for Diagnosing Tuberculosis Using Adaptive Neuro-Fuzzy

Cited by 6 publications

References 20 publications

An Analysis of Adaptable Intelligent Models for Pulmonary Tuberculosis Detection and Classification

An Analysis of Adaptable Intelligent Models for Pulmonary Tuberculosis Detection and Classification

Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications

Image Processing Approach to Determine the Severity Level of Tuberculosis

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