An efficient framework for identification of Tuberculosis and Pneumonia in chest X-ray images using Neural Network

Verma, Devvret; Bose, Chitransh; Tufchi, Neema; Pant, Kumud; Tripathi, Vikas; Thapliyal, Ashish

doi:10.1016/j.procs.2020.04.023

Cited by 54 publications

(29 citation statements)

References 18 publications

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“…121 layered pre-trained DenseNet architecture (Chexnet) was utilized in [25] to detect pneumonia in 112,120 X-ray images of 30,805 unique patients; this model is then extended to detect 14 diseases in X-ray images. In [26] , t he Authors used a pre-trained inceptionV3 for the extraction of image embeddings and an artificial neural network for classification. The said structure was able to classify and segregate distinctive aspiratory diseases proficiently and achieved an extraordinarily high accuracy of 99.01%.…”

Section: Related Workmentioning

confidence: 99%

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Gupta

Anjum

Gupta

et al. 2021

Applied Soft Computing

187

121

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Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19’s ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.

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Section: Related Workmentioning

confidence: 99%

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Gupta

Anjum

Gupta

et al. 2021

Applied Soft Computing

187

121

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“…Verma et al [118] proposed a customized CNN to classify lung nodule in CXR images into three classes: pulmonary TB, viral pneumonia, and bacterial pneumonia. The authors [118] preprocessed the images via data augmentation to avoid overfitting. The experimental results indicated an overall accuracy of 99.01%, but the experimental details were missing from the study.…”

Section: ) Customized Cnnmentioning

confidence: 99%

“…Researchers have mainly focused on DL algorithms for pneumonia detection, but preprocessing techniques, such as image enhancement, data augmentation techniques (classic and synthetic data augmentation) need to be considered to improve the classification performance [89] [91] [105][118]. Pre-processing of CXRs helps in improving the quality of input image by eliminating noise, adjust low or high frequencies, adjusting image contrast, etc.…”

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confidence: 99%

Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review

2021

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Chest radiography is a significant diagnostic tool used to detect diseases afflicting the chest. The automatic detection techniques associated with computer vision are being adopted in medical imaging research. Over the last decade, several remarkable advancements have been made in the field of medical diagnostics with the application of deep learning techniques. Various automated systems have been proposed for the rapid detection of pneumonia from chest X-rays. Although several algorithms are currently available for pneumonia detection, a detailed review summarizing the literature and offering guidelines for medical practitioners is lacking. This study will help practitioners to select the most effective and efficient methods from a real-time perspective, review the available datasets, and understand the results obtained in this domain. It will also present an overview of the literature on intelligent pneumonia identification from chest X-rays. The usability, goodness factors, and computational complexities of the algorithms employed for intelligent pneumonia identification are analyzed. Additionally, this study discusses the quality, usability, and size of the available chest X-ray datasets and techniques for coping with unbalanced datasets. A detailed comparison of the available studies reveals that the majority of the applied datasets are highly unbalanced and limited, providing unreliable results and rendering methods that are unsuitable for large-scale use. Large-scale balanced datasets can be obtained via smart techniques, such as generative adversarial networks. Current literature has indicated that deep learning-based algorithms achieve the best results for pneumonia classification with an accuracy of 98.7%, a sensitivity of 0.99, and a specificity of 0.98. The higher accuracy offered by deep-learning algorithms in addition to their appropriate class balancing techniques serves as a good reference for further research.

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“…Ключові слова: генетичний алгоритм, еволюційний алгоритм, рентгенограма, розпізнавання зображень, neural network, long short -term memory. У 2016 р. Всесвітня організація охорони здоров'я (ВООЗ) включила їх до числа 10 провідних причин смертності у світі [1].…”

Section: особливості побудови рішень генетичного алгоритму в задачі рunclassified

Особливості Побудови Рішень Генетичного Алгоритму В Задачі Розпізнавання Образів Захворювання Пневмонії

Fedorchenko¹,

Oliinyk²,

Korniienko³

et al. 2020

РЗОД

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An efficient framework for identification of Tuberculosis and Pneumonia in chest X-ray images using Neural Network

Cited by 54 publications

References 18 publications

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Intelligent Pneumonia Identification From Chest X-Rays: A Systematic Literature Review

Особливості Побудови Рішень Генетичного Алгоритму В Задачі Розпізнавання Образів Захворювання Пневмонії

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