Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

Gao, Xiaohong; James-Reynolds, Carl; Currie, Edward H.

doi:10.1016/j.neucom.2018.12.086

Cited by 63 publications

(28 citation statements)

References 24 publications

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“…Bhandary et al [20] recommended a method to diagnose other respiratory disorder with the help of DL platform. Gao et al [21] employed 3D block-based residual deep learning framework to detect severe stages of tuberculosis in CT scan and lungs' X-ray imaging. Singh et al [22] introduced particle swarm optimization related to adaptive neuro-fuzzy inference system (ANFIS) to improve the rate of classification.…”

Section: Related Workmentioning

confidence: 99%

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

Shankar

Perumal

2020

Complex Intell. Syst.

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COVID-19 pandemic is increasing in an exponential rate, with restricted accessibility of rapid test kits. So, the design and implementation of COVID-19 testing kits remain an open research problem. Several findings attained using radio-imaging approaches recommend that the images comprise important data related to coronaviruses. The application of recently developed artificial intelligence (AI) techniques, integrated with radiological imaging, is helpful in the precise diagnosis and classification of the disease. In this view, the current research paper presents a novel fusion model hand-crafted with deep learning features called FM-HCF-DLF model for diagnosis and classification of COVID-19. The proposed FM-HCF-DLF model comprises three major processes, namely Gaussian filtering-based preprocessing, FM for feature extraction and classification. FM model incorporates the fusion of handcrafted features with the help of local binary patterns (LBP) and deep learning (DL) features and it also utilizes convolutional neural network (CNN)-based Inception v3 technique. To further improve the performance of Inception v3 model, the learning rate scheduler using Adam optimizer is applied. At last, multilayer perceptron (MLP) is employed to carry out the classification process. The proposed FM-HCF-DLF model was experimentally validated using chest X-ray dataset. The experimental outcomes inferred that the proposed model yielded superior performance with maximum sensitivity of 93.61%, specificity of 94.56%, precision of 94.85%, accuracy of 94.08%, F score of 93.2% and kappa value of 93.5%.

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Section: Related Workmentioning

confidence: 99%

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

Shankar

Perumal

2020

Complex Intell. Syst.

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“…Figure 5 shows examples of CT scan images taken from numerous datasets. CT scans have been used to detect lung disease in numerous work found in the literature, for example for tuberculosis detection [ 24 ], lung cancer detection [ 25 ] and COVID-19 detection [ 26 ].…”

Section: The Taxonomy Of State-of-the-art Work On Lung Disease Detection Using Deep Learningmentioning

confidence: 99%

“…The model then analyses the region of interest within the image to perform the classification of tuberculosis. A research study explores the use of CT pulmonary images to diagnose and classify tuberculosis at five levels of severity to track treatment effectiveness [ 24 ]. The tuberculosis abnormalities only occupy limited regions in the CT image, and the dataset is quite small.…”

Section: The Taxonomy Of State-of-the-art Work On Lung Disease Detection Using Deep Learningmentioning

confidence: 99%

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

et al. 2020

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The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.

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“…The method, named amalgam intellectual scheme, is used to model information, reasoning, and learning in vague and indeterminate neighbors. ANN is developed for further modifications, and the fuzzy system acquires the learning capability 10,11 …”

Section: Introductionmentioning

confidence: 99%

Severity detection and infection level identification of tuberculosis using deep learning

Chithra

Jagatheeswari

2020

Int J Imaging Syst Tech

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Tuberculosis (TB) is a highly infectious disease and is one of the major health problems all over the world. The accurate detection of TB is a major challenge faced by most of the existing methods. This work addresses these issues and developed an effective mechanism for detecting TB using deep learning. Here, the color space transformation is applied for transforming the red green and blue image to LUV space, where L stands for luminance, U and V represent chromaticity values of color images. Then, adaptive thresholding is carried out for image segmentation and various features, like coverage, density, color histogram, area, length, and texture features, are extracted to enable effective classification. After the feature extraction, the size of the features is reduced using principal component analysis. The extracted features are subjected to fractional crow search‐based deep convolutional neural network (FC‐SVNN) for the classification. Then, the image level features, like bacilli count, bacilli area, scattering coefficients and skeleton features are considered to perform severity detection using proposed adaptive fractional crow (AFC)‐deep CNN. Finally, the inflection level is determined using entropy, density and detection percentage. The proposed AFC‐Deep CNN algorithm is designed by modifying FC algorithm using self‐adaptive concept. The proposed AFC‐Deep CNN shows better performance with maximum accuracy value as 0.935.

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Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

Abstract: Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture. Neurocomputing, 392 . pp. 233-244.

Cited by 63 publications

References 24 publications

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

Severity detection and infection level identification of tuberculosis using deep learning

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