Automated identification of mycobacterium bacillus from sputum images for tuberculosis diagnosis

Mithra, K. S.; Emmanuel, W. R. Sam

doi:10.1007/s11760-019-01509-1

Cited by 35 publications

(15 citation statements)

References 21 publications

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“…According to WHO the research on DSS TB is one of the most urgent implementation studies required today [3][4][5]. Several studies based on image processing [6][7][8][9][10][11][12] have been carried out to detect MTB and so far the CNN method [13][14][15] shows the best performance among other methods [14,16,17].…”

Section: Introductionmentioning

confidence: 99%

Covid-19 and Tuberculosis Classification Based on Chest X-Ray Using Convolutional Neural Network

Aulia

Hadiyoso

Mengko

et al. 2021

Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics

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The high rate of patients with tuberculosis (TB) with the graph showing a continual increase requires the research in any sector as the programs to eradicate tuberculosis. One of the applications is the Decision Support System (DSS) that helps the medical experts particularly doctors in diagnosing TB grade 1+. 2+ , and 3+ rapidly. Another problem is related to the imbalance between the number of patients and the number of medical practitioners in the condition of pandemic Corona Virus Disease (Covid-19) today. Hence, DSS is highly required and it can be used for the long-term management of Covid. In this study, the rapid classification of normal lung, tuberculosis lung, and Covid-19 lung based on the Chest X-Ray (CXR) image was proposed as the initial step of DSS implementation. The proposed image processing based CXR classification using Deep Learning Convolutional Neural Network (CNN) obtained the highest accuracy rate of 88.37%. This accuracy was obtained in the second scenario with the 208 CXR datasets. The small number of datasets used was related to the limited number of CXR Covid-19 images with good quality brightness. The proposed system developed is expected to help doctors in diagnose lung disease.

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Section: Introductionmentioning

confidence: 99%

Covid-19 and Tuberculosis Classification Based on Chest X-Ray Using Convolutional Neural Network

Aulia

Hadiyoso

Mengko

et al. 2021

Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics

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“…Mithra et al [ 54 ] proposed DL based technique for identifying and counting tuberculosis bacteria in sputum smear images. The dataset consisted of 500 sputum images.…”

Section: In Microorganisms Image Recognitionmentioning

confidence: 99%

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Rani

Kotwal

Manhas

et al. 2021

Arch Computat Methods Eng

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Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995–2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.

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“…To perform sputum smear examination, a very thin layer of the sputum sample is positioned on a glass slide [ 27 ]. Among the papers surveyed, only five used sputum smear microscopy image [ 28 , 29 , 30 , 31 , 32 ]. Figure 6 shows examples of sputum smear microscopy images.…”

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

confidence: 99%

“…DBN can be used to recognise objects in images, video sequences and motion-capture data. More details of DBN can be found in [ 31 , 48 ].…”

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

confidence: 99%

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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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Automated identification of mycobacterium bacillus from sputum images for tuberculosis diagnosis

Cited by 35 publications

References 21 publications

Covid-19 and Tuberculosis Classification Based on Chest X-Ray Using Convolutional Neural Network

Covid-19 and Tuberculosis Classification Based on Chest X-Ray Using Convolutional Neural Network

Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

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

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