A Novel Architecture for Improving Tuberculosis Detection from Microscopic Sputum Smear Images

Angayarkanni, S. Pitchumani; Vanitha, V.; Karan, V.; Sivant, M.

doi:10.1007/978-3-031-12413-6_5

Cited by 3 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In assessing the chest radiograph to assess the strength of the exposure can be seen from the lower thoracic spine in which images are taken during inspiration (diaphragm at the level of the 5th or 6th rib anterior) and rotation (processus spinosus of the upper thoracic vertebra is felt in the middle of the medial end of the clavicle). Based on the radiologists' reading of the radiographs, most diagnose in the form of infiltrates in unilateral lung apex or the presence of cavities, pleural effusions, infiltrates in the inferior lobes, and lymphadenopathy images without cavitation and infiltrates in the upper lobes of the lungs in which lymphadenopathy is the most common manifestation of TB [28].This image is formed due to the presence of a primary complex, namely a combination of primary focus, enlarged regional lymph nodes (lymphadenitis), and inflamed lymph ducts (lymphangitis) [18].…”

Section: Results Of Radiograph Optical Density In Pneumonia (Tb) Casesmentioning

confidence: 99%

Determination Image Quality on Thorax COVID-19 and Tuberculosis Using Optical Density Image Analysis

Sari¹,

Mayani²,

Putri³

et al. 2023

AMM

View full text Add to dashboard Cite

Covid-19 is a type of pneumonia disease currently affecting people around the world. Covid-19 and tuberculosis are lung diseases that are dangerous and spread quickly to other humans. Chest radiography is one of the main modalities in the management of suspected COVID-19 and tuberculosis patients as it provides radiological information on lung infections that can be used as diagnostic guidelines and patient care. This study aims to determine the differences in optical density and contrast in Covid-19 and pneumonia tuberculosis cases in order to find the specific characteristics of Covid-19 and tuberculosis. This research processed secondary data from Covid-19 and tuberculosis positive patients using image-J software that can be easily obtained and operated by anyone. After processing, the density and contrast were analyzed, particularly for the lungs. Based on image processing results, the average density for Cnovid-19 radiographs is 1,066, while for Tuberculosis radiograph is 1,519. The average contrast values for Covid-19 and tuberculosis radiographs are 0.37 and 1.03. Thus, it can be concluded that the contrast of the Covid-19 radiograph is lower than the tuberculosis radiograph. The difference in optical density on the tuberculosis and Covid-19 cases is 0.5 as the opacity on the chest radiographs of Covid-19 patients is evenly distributed over the entire lung surface.

show abstract

Section: Results Of Radiograph Optical Density In Pneumonia (Tb) Casesmentioning

confidence: 99%

Determination Image Quality on Thorax COVID-19 and Tuberculosis Using Optical Density Image Analysis

Sari¹,

Mayani²,

Putri³

et al. 2023

AMM

View full text Add to dashboard Cite

show abstract

“…Furthermore, the U-Net proposed by Ronneberger et al [ 13 ] adds the “skip connection” to FCN to alleviate the loss of shallow information, which has demonstrated excellent performance on many segmentation tasks, and U-Net-based methods have been applied to many segmentation tasks [ 14 – 16 ]. Angayarkanni et al [ 17 ] enhanced the quality of sputum smears using several image preprocessing methods and then fed the preprocessed images to U-Net for Mycobacterium tuberculosis segmentation. Ali et al [ 18 ] proposed a modified U-Net and applied it to the segmentation of bacilli in ZN-stained sputum smear images.…”

Section: Introductionmentioning

confidence: 99%

DB-DCAFN: dual-branch deformable cross-attention fusion network for bacterial segmentation

Wang

Cao

et al. 2023

Vis. Comput. Ind. Biomed. Art

View full text Add to dashboard Cite

Sputum smear tests are critical for the diagnosis of respiratory diseases. Automatic segmentation of bacteria from sputum smear images is important for improving diagnostic efficiency. However, this remains a challenging task owing to the high interclass similarity among different categories of bacteria and the low contrast of the bacterial edges. To explore more levels of global pattern features to promote the distinguishing ability of bacterial categories and maintain sufficient local fine-grained features to ensure accurate localization of ambiguous bacteria simultaneously, we propose a novel dual-branch deformable cross-attention fusion network (DB-DCAFN) for accurate bacterial segmentation. Specifically, we first designed a dual-branch encoder consisting of multiple convolution and transformer blocks in parallel to simultaneously extract multilevel local and global features. We then designed a sparse and deformable cross-attention module to capture the semantic dependencies between local and global features, which can bridge the semantic gap and fuse features effectively. Furthermore, we designed a feature assignment fusion module to enhance meaningful features using an adaptive feature weighting strategy to obtain more accurate segmentation. We conducted extensive experiments to evaluate the effectiveness of DB-DCAFN on a clinical dataset comprising three bacterial categories: Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The experimental results demonstrate that the proposed DB-DCAFN outperforms other state-of-the-art methods and is effective at segmenting bacteria from sputum smear images.

show abstract

Pulmonary Tuberculosis Diagnosis Using an Intelligent Microscopy Scanner and Image Recognition Model for Improved Acid-Fast Bacilli Detection in Smears

Chen,

Chang,

Lin

2024

Microorganisms

View full text Add to dashboard Cite

Microscopic examination of acid-fast mycobacterial bacilli (AFB) in sputum smears remains the most economical and readily available method for laboratory diagnosis of pulmonary tuberculosis (TB). However, this conventional approach is low in sensitivity and labor-intensive. An automated microscopy system incorporating artificial intelligence and machine learning for AFB identification was evaluated. The study was conducted at an infectious disease hospital in Jiangsu Province, China, utilizing an intelligent microscope system. A total of 1000 sputum smears were included in the study, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFBs. Referee technicians served as the gold standard for discrepant results. The automated system demonstrated an overall accuracy of 96.70% (967/1000), sensitivity of 91.94% (194/211), specificity of 97.97% (773/789), and negative predictive value (NPV) of 97.85% (773/790) at a prevalence of 21.1% (211/1000). Incorporating AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

show abstract

A Novel Architecture for Improving Tuberculosis Detection from Microscopic Sputum Smear Images

Cited by 3 publications

References 17 publications

Determination Image Quality on Thorax COVID-19 and Tuberculosis Using Optical Density Image Analysis

Determination Image Quality on Thorax COVID-19 and Tuberculosis Using Optical Density Image Analysis

DB-DCAFN: dual-branch deformable cross-attention fusion network for bacterial segmentation

Pulmonary Tuberculosis Diagnosis Using an Intelligent Microscopy Scanner and Image Recognition Model for Improved Acid-Fast Bacilli Detection in Smears

Contact Info

Product

Resources

About