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

Siddiqui, Abdul Karim; Garg, Vijay Kumar

doi:10.1007/s42979-021-00890-4

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…The findings of medical test results and domain knowledge are combined by a vast variety of AI-based disease detection and classification systems [11,12]. Recent research has shown how AI can be used to classify lung tuberculosis, identify benign or malignant melanoma, diagnose COVID 19 using a chest X-ray, and detect the progression of retinal illness [13][14][15][16]. Ophthalmic disorders are typically not life-threatening, but they can have a major impact on the patient's quality of life as they advance over time.…”

Section: Introductionmentioning

confidence: 99%

The classification of eye diseases from fundus images based on CNN and pretrained models

Benbakreti,

Ozkaya

2024

Acta Polytech

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Visual impairment affects more than a billion people worldwide due to insufficient care or inadequate vision screening. Computer-aided diagnosis using deep neural networks is a promising approach, it can analyse and process retinal fundus images, providing valuable reference data for doctors in clinical diagnosis or screening. This study aims to achieve an accurate classification of fundus images, including images of healthy patients as well as those with diabetic retinopathy, cataracts, and glaucoma, using a convolutional neural network (CNN) architecture and several pretrained models (AlexNet, GoogleNet, ResNet18, ResNet50, YOLOv3, and VGG 19). To enhance the training process, a mirror effect technique was applied to augment the volume of data. The experimental study resulted in very satisfactory outcomes, with the GoogleNet model paired with the SGDM optimiser achieving the highest accuracy (92.7 %).

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Section: Introductionmentioning

confidence: 99%

The classification of eye diseases from fundus images based on CNN and pretrained models

Benbakreti,

Ozkaya

2024

Acta Polytech

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“…The application of deep learning to the prognosis of tuberculosis exceeds other standard machine learning techniques. Using genetic profiles and phenotype data, deep learning frameworks have also been applied to the diagnosis, classification, and therapy of tuberculosis [25].…”

Section: Introductionmentioning

confidence: 99%

Tuberculosis Disease Detection through CXR Images based on Deep Neural Network Approach

Patil,

Gadekar

2023

IJRITCC

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Tuberculosis (TB) is a disease that, if left untreated for an extended period of time, can ultimately be fatal. Early TB detection can be aided by using a deep learning ensemble. In previous work, ensemble classifiers were only trained on images that shared similar characteristics. It is necessary for an ensemble to produce a diverse set of errors in order for it to be useful; this can be accomplished by making use of a number of different classifiers and/or features. In light of this, a brand-new framework has been constructed in this study for the purpose of segmenting and identifying TB in human Chest X-ray. It was determined that searching traditional web databases for chest X-ray was necessary. At this point, we pass the photos that we have collected over to Swin ResUnet3 so that they may be segmented. After the segmented chest X-ray have been provided to it, the Multi-scale Attention-based Densenet with Extreme Learning Machine (MAD-ELM) model will be applied in the detection stage in order to effectively diagnose tuberculosis from human chest X-ray. This will be done in order to maximize efficiency. Because it increased the variety of errors made by the basic classifiers, the supplied variation of the approach that was proposed was able to detect tuberculosis more effectively. The proposed ensemble method produced results with an accuracy of 94.2 percent, which are comparable to those obtained by past efforts.

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“…Neural network techniques consist of multiple hidden layers, and each layer has units connected to each other to transmit information. Meanwhile, deep learning techniques consist of many layers with millions of neurons connected by millions of connections [8]. Deep learning techniques are distinguished by their superior ability to diagnose huge datasets and extract deep features with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion

2022

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Tuberculosis (TB) is a fatal disease in developing countries, with the infection spreading through direct contact or the air. Despite its seriousness, the early detection of tuberculosis by means of reliable techniques can save the patients’ lives. A chest X-ray is a recommended screening technique for locating pulmonary abnormalities. However, analyzing the X-ray images to detect abnormalities requires highly experienced radiologists. Therefore, artificial intelligence techniques come into play to help radiologists to perform an accurate diagnosis at the early stages of TB disease. Hence, this study focuses on applying two AI techniques, CNN and ANN. Furthermore, this study proposes two different approaches with two systems each to diagnose tuberculosis from two datasets. The first approach hybridizes two CNN models, which are Res-Net-50 and GoogLeNet techniques. Prior to the classification stage, the approach applies the principal component analysis (PCA) algorithm to reduce the features’ dimensionality, aiming to extract the deep features. Then, the SVM algorithm is used for classifying features with high accuracy. This hybrid approach achieved superior results in diagnosing tuberculosis based on X-ray images from both datasets. In contrast, the second approach applies artificial neural networks (ANN) based on the fused features extracted by ResNet-50 and GoogleNet models and combines them with the features extracted by the gray level co-occurrence matrix (GLCM), discrete wavelet transform (DWT) and local binary pattern (LBP) algorithms. ANN achieved superior results for the two tuberculosis datasets. When using the first dataset, the ANN, with ResNet-50, GLCM, DWT and LBP features, achieved an accuracy of 99.2%, a sensitivity of 99.23%, a specificity of 99.41%, and an AUC of 99.78%. Meanwhile, with the second dataset, ANN, with the features of ResNet-50, GLCM, DWT and LBP, reached an accuracy of 99.8%, a sensitivity of 99.54%, a specificity of 99.68%, and an AUC of 99.82%. Thus, the proposed methods help doctors and radiologists to diagnose tuberculosis early and increase chances of survival.

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

Cited by 5 publications

References 33 publications

The classification of eye diseases from fundus images based on CNN and pretrained models

The classification of eye diseases from fundus images based on CNN and pretrained models

Tuberculosis Disease Detection through CXR Images based on Deep Neural Network Approach

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion

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