Automatic classification of light field smear microscopy patches using Convolutional Neural Networks for identifying mycobacterium tuberculosis

Lopez, Yadini Perez; Filho, Cícero Ferreira Fernandes Costa; Aguilera, Luis Miguel Rojas; Costa, Marly Guimarães Fernandes

doi:10.1109/chilecon.2017.8229512

Cited by 29 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

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

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…In recent years, in the literature, methods have been published to automate the diagnosis of TB. These process a sputum smear microscopy image using a variety of computational techniques: digital image processing [2] and [3]), traditional machine learning algorithms [4] and [5], and shallow neural networks and deep neural networks (such as the convolutional neural networks) [6], [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

“…López et al [7] developed a method also based on CNN for bacilli´s classification. The database inputs used for CNN training and testing is comprised patches of 40x40 pixel size, extracted from smear images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Bacillus Detection in Light Field Microscopy Images Using Convolutional Neural Networks and Mosaic Imaging Approach

Serrao

Costa

Fujimoto

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

View full text Add to dashboard Cite

Tuberculosis (TB) is one of the top 10 causes of death worldwide. The diagnosis and treatment of TB in its early stages is fundamental to reducing the rate of people affected by this disease. In order to assist specialists in the diagnosis in bright field smear images, many studies have been developed for the automatic Mycobacterium tuberculosis detection, the causative agent of Tb. To contribute to this theme, a method to bacilli detection associating convolutional neural network (CNN) and a mosaic-image approach was implemented. The propose was evaluated using a robust image dataset validated by three specialists. Three CNN architectures and 3 optimization methods in each architecture were evaluated. The deeper architecture presented better results, reaching accuracies values above 99%. Other metrics like precision, sensitivity, specificity and F1-score were also used to assess the CNN models performance. Clinical Relevance-The presented works provides an automatic method to aid the diagnosis of tuberculosis in brightfield microscopy.

show abstract

“…Mycobacterium tuberculosis can cause a direct infectious disease namely tuberculosis (TB). Identification of TB through microscopic observation (screening) using sputum smear samples has greatly helped prevent TB disease [1][2][3][4][5][6][7]. But, the process of identifying TB through microscopic screening requires a long time, high accuracy, and expert laboratory personnel.…”

Section: Introductionmentioning

confidence: 99%

Identification the number of Mycobacterium tuberculosis based on sputum image using local linear estimator

et al. 2020

View full text Add to dashboard Cite

Infectious disease caused by infection of Mycobacterium tuberculosis is called tuberculosis (TB). A common method in detecting TB is by identifying number of mycobacterium TB in sputum manually. Unfortunately, manually calculation by pathologists take a relatively long time. Previous researches on TB bacteria were still limited to detect the absence or presence of mycobacterium TB in images of sputum. This research aims are identifying number of mycobacterium TB and determining accuracy of classification TB severity by approaching nonparametric Poisson regression model and applying an estimator namely local linear. Steps include processing of image, reducing of dimension by applying partial least square and discrete wavelet transformation, and then identifying the number of mycobacterium TB by using the proposed model approach. In this research, we get deviance values of 28.410 for nonparametric and 93.029 for parametric approaches and the average of classification accuracy values for 4 iterations of 92.75% for nonparametric and 85.5% for parametric approaches. Thus, for identifying many of mycobacterium TB met in images of sputum and classifying of TB severity, the proposed identifying method gives higher accuracy and shorter time in identifying number of mycobacterium TB than parametric linear regression method.

show abstract

Automatic classification of light field smear microscopy patches using Convolutional Neural Networks for identifying mycobacterium tuberculosis

Cited by 29 publications

References 9 publications

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

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

Automatic Bacillus Detection in Light Field Microscopy Images Using Convolutional Neural Networks and Mosaic Imaging Approach

Identification the number of Mycobacterium tuberculosis based on sputum image using local linear estimator

Contact Info

Product

Resources

About