Lung tuberculosis detection using anti-aliased convolutional networks

Singh, Jodh; Tripathy, Aruna; Garg, Priyam; Kumar, Anupam

doi:10.1016/j.procs.2020.06.033

Cited by 18 publications

(16 citation statements)

References 10 publications

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“…Some researchers have also shown CNN to be a great tool for identification of COVID-19 from CXR radiographs [38]. In [39], authors have suggested anti-aliased convolutional networks for detection of lung diseases. Also, a number of reported work have shown that chest CT scans are an important tool in COVID-19 diagnosis [9,[40][41][42].…”

Section: Related Workmentioning

confidence: 99%

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

et al. 2021

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A novel strain of Coronavirus, identified as the Severe Acute Respiratory Syndrome-2 (SARS-CoV-2), outbroke in December 2019 causing the novel Corona Virus Disease (COVID-19). Since its emergence, the virus has spread rapidly and has been declared a global pandemic. As of the end of January 2021, there are almost 100 million cases worldwide with over 2 million confirmed deaths. Widespread testing is essential to reduce further spread of the disease, but due to a shortage of testing kits and limited supply, alternative testing methods are being evaluated. Recently researchers have found that chest X-Ray (CXR) images provide salient information about COVID-19. An intelligent system can help the radiologists to detect COVID-19 from these CXR images which can come in handy at remote locations in many developing nations. In this work, we propose a pipeline that uses CXR images to detect COVID-19 infection. The features from the CXR images were extracted and the relevant features were then selected using Hybrid Social Group Optimization algorithm. The selected features were then used to classify the CXR images using a number of classifiers. The proposed pipeline achieves a classification accuracy of 99.65% using support vector classifier, which outperforms other state-of-the-art deep learning algorithms for binary and multi-class classification.

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Section: Related Workmentioning

confidence: 99%

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

et al. 2021

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“…They also calculate overall severity probability, separate probabilities of both high severity and low severity forces. Singh et al ( 2020 ) worked to discover tuberculosis sores in the lungs. They proposed a computerized recognition strategy utilizing a profound learning technique known as Antialiased Convolution Neural Network proposed by Richard Zhang.…”

Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

411

173

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Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

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“…In the human population, Tuberculosis (TB) is an infectious disease. In airborne particles, termed droplet nuclei, TB is held from 1 to 5 microns in diameter [1]. Infectious droplet nuclei are produced when people cough, sneeze, yell or sing when they have pulmonary or laryngeal TB disease.…”

Section: Introductionmentioning

confidence: 99%

“…TB is distributed through the air from individual to individual. It is often caused by bacteria known as Mycobacterium tuberculosis, often infected by human lungs [1]. TB is conveyed by everyone or anywhere through the air.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Deep Learning Techniques for Tuberculosis Disease

2021

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The airborne disease is a severe disease in the world that spreads exponentially. An Immunochromatography Test(IT) and Biological Aerosol Particles (BAP) test with a disposable instrument that will be recorded in the literature is a standard diagnostic procedure for respiratory infections, such as influenza and TB, in the investigation causes of suspected disease. This examination helps the examiner to identify infectious patients quickly, efficiently, and inexpensively. Self-diagnosis, however, is problematic because it requires a medical specialist to diagnose the diseases in infections. Chest radiography is also not only the primary screening method that is focused on surgical imaging but also diagnostic radiology. Various methods of classification of Tuberculosis are analyzed here. Computer-Assisted Diagnosis (CAD) has become popular, and many researchers are involved in these fields of study. Numerous methods have been suggested for airborne identification and Classification of lung diseases. The medical history of airborne tuberculosis disease in chest X-rays and a review of the different approaches to diagnosing and Classification of the airborne disease are discussed in this article. According to the literature on the relevant policies, items in this research field have been surveyed until 2020.

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Lung tuberculosis detection using anti-aliased convolutional networks

Cited by 18 publications

References 10 publications

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Analysis of Deep Learning Techniques for Tuberculosis Disease

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