Tuberculosis Bacilli Identification: A Novel Feature Extraction Approach via Statistical Shape and Color Models

Yousefi, Hamed; Mohammadi, Farnaz; Mirian, Niloufar; Amini, Navid

doi:10.1109/icmla51294.2020.00065

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…Em [Yousefi et al 2020], um novo modelo estatístico é proposto para a identificac ¸ão dos bacilos, considerando aspectos de cor e forma e um dataset com 43 imagens de SSM. Os autores aplicaram técnicas de pré-processamento para aprimorar as imagens antes da etapa de segmentac ¸ão.…”

Section: Trabalhos Relacionadosunclassified

EfficientBacillus: uma arquitetura profunda para detecção dos bacilos de Koch

Santos,

Brito,

Carvalho Filho

et al. 2023

Anais Do XXIII Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2023)

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A tuberculose é uma infecção bacteriana causada pelo bacilo de Koch e é transmitida pelo ar. A doença afeta principalmente os pulmões e é considerada a segunda principal causa de morte por infecção no mundo. Apesar disso, a tuberculose tem cura e o diagnóstico precoce é de suma importância para o sucesso do tratamento e para evitar a disseminação da doença. Tradicionalmente, a baciloscopia de esfregaço de escarro tem sido o principal método para o diagnóstico e monitoramento do tratamento da tuberculose. Nesse contexto, diversas abordagens computacionais têm sido desenvolvidas para auxiliar no diagnóstico da tuberculose, através da análise das imagens de baciloscopia. Neste trabalho, propomos a utilização da EfficientDet, explorando cada um dos seus backbones na tarefa de identificação dos bacilos. Também avaliamos 4 representações de cor diferentes e aplicamos uma validação cruzada com k-fold = 5. Os resultados mostraram-se promissores, com IoU de 0,523, recall de 0,925, precision de 0,694 e f1-score de 0,774. Os resultados alcançados evidenciaram o potencial do método na detecção dos bacilos, podendo auxiliar no diagnóstico da tuberculose.

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Section: Trabalhos Relacionadosunclassified

EfficientBacillus: uma arquitetura profunda para detecção dos bacilos de Koch

Santos,

Brito,

Carvalho Filho

et al. 2023

Anais Do XXIII Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2023)

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“…Yousefi et al [59] suggested a novel statistical model of the form and colour of TB bacilli in Ziehl-Neelsen-stained light microscope images in order to detect the bacilli in these images. These basic statistical models were used as a universal library for rebuilding any bacillus with different background colours and may overcome the challenges associated with geometric feature extraction techniques.…”

Section: Image Gradient-based Approachesmentioning

confidence: 99%

Automated Methods for Tuberculosis Detection/Diagnosis: A Literature Review

Zachariou,

Arandjelović,

Sloan

2023

BioMedInformatics

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Tuberculosis (TB) is one of the leading infectious causes of death worldwide. The effective management and public health control of this disease depends on early detection and careful treatment monitoring. For many years, the microscopy-based analysis of sputum smears has been the most common method to detect and quantify Mycobacterium tuberculosis (Mtb) bacteria. Nonetheless, this form of analysis is a challenging procedure since sputum examination can only be reliably performed by trained personnel with rigorous quality control systems in place. Additionally, it is affected by subjective judgement. Furthermore, although fluorescence-based sample staining methods have made the procedure easier in recent years, the microscopic examination of sputum is a time-consuming operation. Over the past two decades, attempts have been made to automate this practice. Most approaches have focused on establishing an automated method of diagnosis, while others have centred on measuring the bacterial load or detecting and localising Mtb cells for further research on the phenotypic characteristics of their morphology. The literature has incorporated machine learning (ML) and computer vision approaches as part of the methodology to achieve these goals. In this review, we first gathered publicly available TB sputum smear microscopy image sets and analysed the disparities in these datasets. Thereafter, we analysed the most common evaluation metrics used to assess the efficacy of each method in its particular field. Finally, we generated comprehensive summaries of prior work on ML and deep learning (DL) methods for automated TB detection, including a review of their limitations.

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“…Hamed et al's research in developing tuberculosis detection using various methods to classify individual and overlapping bacilli from the rest of the images based on the eigenvalues of the shape and color models. By using statistical shape model and statistical color model and KNN classifier produces an average accuracy value of 82.7% for the detection of single bacilli and overlapping bacilli, to identify only individual bacilli from overlapping bacilli and other objects, and the accuracy value is 99, 1% [13].…”

Section: Figure 2 Estimated Impact Of the Covid-19 Pandemic On The Nu...mentioning

confidence: 99%

Convolutional Neural Network as an Image Processing Technique for Classification of Bacilli Tuberculosis Extra Pulmonary (TBEP) Disease

Riza¹,

Na’am²

2022

TEM Journal

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Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting for a long time. Biopsy is one of the techniques used to take the patient's lung fluid and given Ziehl Neelsen chemical dye and then observed using a microscope to determine this TBEP disease. In this research, the TBEP detection process was developed using a classification method, namely CNN with feature extraction and feature selection. The feature uses 5 features where these features are a combination of shape features and texture features with the highest information gain value. From the results of research conducted through the training and testing stages of the classification method using feature selection, the accuracy rate is higher than not using feature selection with a comparison of the feature selection stage increasing 0.6536% for the training process, and 0.8942% for the testing process.

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Tuberculosis Bacilli Identification: A Novel Feature Extraction Approach via Statistical Shape and Color Models

Cited by 8 publications

References 18 publications

EfficientBacillus: uma arquitetura profunda para detecção dos bacilos de Koch

EfficientBacillus: uma arquitetura profunda para detecção dos bacilos de Koch

Automated Methods for Tuberculosis Detection/Diagnosis: A Literature Review

Convolutional Neural Network as an Image Processing Technique for Classification of Bacilli Tuberculosis Extra Pulmonary (TBEP) Disease

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