Modified Fuzzy Q Learning Based Classifier for Pneumonia and Tuberculosis

Kukker, Amit; Sharma, Rajneesh

doi:10.1016/j.irbm.2020.10.005

Cited by 13 publications

(10 citation statements)

References 13 publications

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“…Despite the use of binary categorization [ 19 ] in this study, other kinds of drug-resistant tuberculosis, including DS, MDR, and XDR, are predicted. Several machine learning algorithms, including multi-layer perceptron [ 20 , 21 ], K-nearest neighbor [ 22 , 23 ], and support vector machine [ 23 , 24 ] were used to identify the solution.…”

Section: Related Workmentioning

confidence: 99%

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

et al. 2022

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A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17–43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after thae trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.

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Section: Related Workmentioning

confidence: 99%

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

et al. 2022

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“…Images of tuberculosis, pneumonia, and normal CXRs were classified using reinforcement learning in Kukker, A. and Sharma, R. [ 23 ]. The fuzzy Q learning method is used with wavelet-based preprocessing to create a classifier for determining the severity of tuberculosis and pneumonia, and an average accuracy of 96.75% was obtained.…”

Section: Introductionmentioning

confidence: 99%

Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System

et al. 2022

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This research develops the TB/non-TB detection and drug-resistant categorization diagnosis decision support system (TB-DRC-DSS). The model is capable of detecting both TB-negative and TB-positive samples, as well as classifying drug-resistant strains and also providing treatment recommendations. The model is developed using a deep learning ensemble model with the various CNN architectures. These architectures include EfficientNetB7, mobileNetV2, and Dense-Net121. The models are heterogeneously assembled to create an effective model for TB-DRC-DSS, utilizing effective image segmentation, augmentation, and decision fusion techniques to improve the classification efficacy of the current model. The web program serves as the platform for determining if a patient is positive or negative for tuberculosis and classifying several types of drug resistance. The constructed model is evaluated and compared to current methods described in the literature. The proposed model was assessed using two datasets of chest X-ray (CXR) images collected from the references. This collection of datasets includes the Portal dataset, the Montgomery County dataset, the Shenzhen dataset, and the Kaggle dataset. Seven thousand and eight images exist across all datasets. The dataset was divided into two subsets: the training dataset (80%) and the test dataset (20%). The computational result revealed that the classification accuracy of DS-TB against DR-TB has improved by an average of 43.3% compared to other methods. The categorization between DS-TB and MDR-TB, DS-TB and XDR-TB, and MDR-TB and XDR-TB was more accurate than with other methods by an average of 28.1%, 6.2%, and 9.4%, respectively. The accuracy of the embedded multiclass model in the web application is 92.6% when evaluated with the test dataset, but 92.8% when evaluated with a random subset selected from the aggregate dataset. In conclusion, 31 medical staff members have evaluated and utilized the online application, and the final user preference score for the web application is 9.52 out of a possible 10.

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“…Pneumonia is a respiratory disease of the thoracic that commonly affects many people frequently characterized by the presence of fluid in the air sacs of the lungs or alveoli [1]. The most common symptoms of patients with pneumonia include cough, shortness of breath, and fever [2].…”

Section: Introductionmentioning

confidence: 99%

“…Pneumonia typically infects children as well as the elderly. The threat from pneumonia has been reported from a World Health Organization (WHO) report in 2017 which stated that approximately 18% of child deaths worldwide are caused by pneumonia [1]. Several cases of pneumonia are caused by bacteria commonly found in hospitals [4].…”

Section: Introductionmentioning

confidence: 99%

“…Pneumonia detection is performed in three ways, which includes: X-Ray, CT-Scan, and multi-modal based method [4]. X-Rays are the most commonly used way to identify pneumonia because of its low cost, can be found in almost all health facilities [1]. However, the detection of pneumonia is challenging due to other underlying lung problems, such as: lung edema, bleeding, and lung cancer or surgical intervention [6].…”

Section: Introductionmentioning

confidence: 99%

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Pneumonia Classification using Gabor-Convolutional Neural Networks and Image Enhancement

Minarno

Alfarizy

Hendryawan

et al. 2021

2021 9th International Conference on Information and Communication Technology (ICoICT)

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Pneumonia is acknowledged as a respiratory disease caused by bacterial and, viral or fungal infections and has a high mortality rate. Identification of pneumonia is typically performed with Chest X-Ray image, but hampered by other lung problems that have been experienced by the patient. Therefore, this study proposes a Convolutional Neural Networks method by adding a Gabor filter and an Image Enhancement Preprocessing technique. The application of the Gabor filter obtains the best accuracy with a value of 94.4% and a loss of 44%, while Image Enhancement obtains an accuracy of 87.8% and the best loss of 35.8%. Combining the Gabor filter and Image Enhancement obtains better accuracy and loss of 93.9% and 40% than utilizing these methods separately.

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Modified Fuzzy Q Learning Based Classifier for Pneumonia and Tuberculosis

Cited by 13 publications

References 13 publications

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System

Pneumonia Classification using Gabor-Convolutional Neural Networks and Image Enhancement

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