Effective Analysis of Lung Infection using Fuzzy Rules

Walia, Navneet; Singh, Harjit; Sharma, Anurag

doi:10.11591/ijai.v5.i2.pp55-63

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…There is also a lack of radiology interpretation expertise in TB prevalent places [5]. A system to semi-automatically classifying pulmonary nodules with low false positivity is thus considered necessary to help radiologists screens the chest x-ray images [6], as medical diagnosis is one of the most important issues in healthcare [7]. Image processing has shown potential in tuberculosis detection.…”

Section: Introductionmentioning

confidence: 99%

Ensemble deep learning for tuberculosis detection using chest X-ray and canny edge detected images

Hwa

Hijazi

Bade

et al. 2019

IJ-AI

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Tuberculosis (TB) is a disease caused by Mycobacterium Tuberculosis. Detection of TB at an early stage reduces mortality. Early stage TB is usually diagnosed using chest x-ray inspection. Since TB and lung cancer mimic each other, it is a challenge for the radiologist to avoid misdiagnosis. This paper presents an ensemble deep learning for TB detection using chest x-ray and Canny edge detected images. This method introduces a new type of feature for the TB detection classifiers, thereby increasing the diversity of errors of the base classifiers. The first set of features were extracted from the original x-ray images, while the second set of features were extracted from the edge detected image. To evaluate the proposed approach, two publicly available datasets were used. The results show that the proposed ensemble method produced the best accuracy of 89.77%, sensitivity of 90.91% and specificity of 88.64%. This indicates that using different types of features extracted from different types of images can improve the detection rate.

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Section: Introductionmentioning

confidence: 99%

Ensemble deep learning for tuberculosis detection using chest X-ray and canny edge detected images

Hwa

Hijazi

Bade

et al. 2019

IJ-AI

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“…The fuzzy logics in the field of health care have been widely used and applied in wide ranging of topic from disease diagnosis (Walia et al . 2016, Walia et al .…”

Section: Discussionmentioning

confidence: 99%

“…2020), and diabetes risk assessment (Allahverdi and Ertosun 2016). Walia et al . (2016) estimated that fuzzy set for haemoptysis value of 0.5 and cough 40, the fuzzy system generates output 0.88 which corresponds to a probability of lung infection.…”

Section: Discussionmentioning

confidence: 99%

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Fuzzy logic assisted COVID 19 safety assessment of dental care

Adwibowo¹

2020

Preprint

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Uncertainty is significant when assessing a risk of certain health care facility conditions especially the facility that prone to the COVID 19 risk. One solution to deal with an uncertainty in health situation assessment is through fuzzy inference system. For that reason, this study aims to develop fuzzy assisted system to assess the safety of dental care related to the sets of patient and environmental conditions. The fuzzy system allows assessment based on the patient body temperature, travel history, dental care ventilation rate, and disinfection frequency. The fuzzy system incorporates several steps including fuzzification, fuzzy regulation, and defuzzification. As a result of this study, the fuzzy system is able to assess and identify the risk of dental care according to the patient health status and hygiene conditions of dental care as well. To conclude, fuzzy system used in this study has offered the advantage of assessing at any situation as for patient and environmental factor predicts the safety of dental care.

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“…Second, there is an insufficiency of expert radiograph readers in high-TB-burden areas [4]. Therefore, a semi-automated TB detection system that can support medical diagnosis is necessary to provide better healthcare to society [5].…”

Section: Introductionmentioning

confidence: 99%

Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images

Hwa

Abdullah

Hijazi

et al. 2020

IJ-AI

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Tuberculosis (TB) is a disease that causes death if not treated early. Ensemble deep learning can aid early TB detection. Previous work trained the ensemble classifiers on images with similar features only. An ensemble requires a diversity of errors to perform well, which is achieved using either different classification techniques or feature sets. This paper focuses on the latter, where TB detection using deep learning and contrast-enhanced canny edge detected (CEED-Canny) x-ray images is presented. The CEED-Canny was utilized to produce edge detected images of the lung x-ray. Two types of features were generated; the first was extracted from the Enhanced x-ray images, while the second from the Edge detected images. The proposed variation of features increased the diversity of errors of the base classifiers and improved the TB detection. The proposed ensemble method produced a comparable accuracy of 93.59%, sensitivity of 92.31% and specificity of 94.87% with previous work.

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Effective Analysis of Lung Infection using Fuzzy Rules

Cited by 5 publications

References 17 publications

Ensemble deep learning for tuberculosis detection using chest X-ray and canny edge detected images

Ensemble deep learning for tuberculosis detection using chest X-ray and canny edge detected images

Fuzzy logic assisted COVID 19 safety assessment of dental care

Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images

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