Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods

Panicker, Rani Oomman; Kalmady, Kaushik S.; Rajan, Jeny; Sabu, M. K.

doi:10.1016/j.bbe.2018.05.007

Cited by 107 publications

(64 citation statements)

References 6 publications

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“…Panicker et al 23 designed an automatic method for TB detection using bacilli from microscopic sputum smear images. The WHO says that TB is the ninth factor that leads to death from all over the world.…”

Section: Literature Surveymentioning

confidence: 99%

“…The total number of TB cases requires on‐time treatments with improved accuracy. The manual detection of TB is a complex task, and highly skilled lab technicians are needed for diagnosing TB 23 The analysis of Mycobacterium image by screening the image in microscopy is a complicated task due to varying images while processing images 25 …”

Section: Literature Surveymentioning

confidence: 99%

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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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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Severity detection and infection level identification of tuberculosis using deep learning

Chithra

Jagatheeswari

2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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“…Likewise, in order to give solidity to the obtained objects, the morphological closing process [19] is applied with a structural element EE. This process is indicated in (14).…”

Section: Segmentationmentioning

confidence: 99%

“…Panicker et al [14] in 2018, present an automatic method for the detection of Tuberculosis (TB) bacilli by image binarization and subsequent classification of regions detected using a convolutional neural network. They evaluated the algorithm using a data set of 22 images with different backgrounds (high density and low density images).…”

Section: Introductionmentioning

confidence: 99%

An algorithm for detection of tuberculosis bacilli in Ziehl-Neelsen sputum smear images

Carpio¹,

Dianderas²,

Zimic³

et al. 2019

IJECE

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This work proposes an algorithm oriented to the detection of tuberculosis bacilli in digital images of sputum samples, inked with the Ziehl Neelsen method and prepared with the direct, pellet and diluted pellet methods. The algorithm aims at automating the optical analysis of bacilli count and the calculation of the concentration level. Several algorithms have been proposed in the literature with the same objective, however, in no case is the performance in sensitivity and specificity evaluated for the 3 preparation methods. The proposed algorithm improves the contrast of the colors of interest, then thresholds the image and segments by labeling the objects of interest (bacilli). Each object then has its geometrical descriptors and photometric descriptors. With all this, a characteristic vector is formed, which are used in the training and classification process of an SVM. For the training 225 images obtained by the 3 preparation methods were used. The proposed algorithm reached, for the direct method, a sensitivity level of 93.67% and a specificity level of 89.23%. In the case of the Pellet method, a sensitivity of 92.13% and a specificity of 82.58% was obtained, while for diluted Pellet the sensitivity was 92.81% and the specificity 83.61%.

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“…Panicker et al [14] present an automatic method for the detection of Tuberculosis (TB) bacilli from microscopic sputum smear images. The proposed method performs detection of TB, by image binarization and subsequent classification of detected regions using a CNN.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Different CNN Models in Tuberculosis Detecting

2020

KSII TIIS

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Tuberculosis is a chronic and delayed infection which is easily experienced by young people. According to the statistics of the World Health Organization (WHO), there are nearly ten million fell ill with tuberculosis and a total of 1.5 million people died from tuberculosis in 2018 (including 251000 people with HIV). Tuberculosis is the largest single infectious pathogen that leads to death. In order to help doctors with tuberculosis diagnosis, we compare the tuberculosis classification abilities of six popular convolutional neural network (CNN) models in the same data set to find the best model. Before training, we optimize three parts of CNN to achieve better results. We employ sigmoid function to replace the step function as the activation function. What's more, we use binary cross entropy function as the cost function to replace traditional quadratic cost function. Finally, we choose stochastic gradient descent (SGD) as gradient descent algorithm. From the results of our experiments, we find that Densenet121 is most suitable for tuberculosis diagnosis and achieve a highest accuracy of 0.835. The optimization and expansion depend on the increase of data set and the improvements of Densenet121.

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Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods

Cited by 107 publications

References 6 publications

Severity detection and infection level identification of tuberculosis using deep learning

Severity detection and infection level identification of tuberculosis using deep learning

An algorithm for detection of tuberculosis bacilli in Ziehl-Neelsen sputum smear images

Comparison of Different CNN Models in Tuberculosis Detecting

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