A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases

Ma, Ling; Liu, Xiabi; Fei, Baowei

doi:10.1007/s11517-020-02146-4

Cited by 9 publications

(9 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They equated the suggested categorization to the conventional (PTB, E-PTB) in an ally of HIV-infected and HIV-uninfected newborns with culture-confirmed TB as a first application. In [25] worked out the identification of normal disorders in lung CT images using a multistep similarity method. This measurement of similarity is then categorized into scales of low, mid-level, and high-level.…”

Section: Related Workmentioning

confidence: 99%

Ensembling of Efficient Deep Convolutional Networks and Machine Learning Algorithms for Resource Effective Detection of Tuberculosis Using Thoracic (Chest) Radiography

Mehrotra¹,

Ansari

Agrawal³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Tuberculosis (TB) is a communicable pulmonary disorder and countries with low and middle-income share a higher TB burden as compared to others. The year 2020-2021 universally saw a brutal pandemic in the form of COVID-19, that crushed various lives, health infrastructures, programs, and economies worldwide at an unprecedented speed. The gravity of this estimation gets intensified in systems with limited technological advancements. To assist in the identification of tuberculosis, we propose the ensembling of efficient deep convolutional networks and machine learning algorithms that do not entail heavy computational resources. In this paper, the three of the most efficient deep convolutional networks and machine learning algorithms are employed for resource-effective (low computational and basic Imaging requirements) detection of Tuberculosis. The pivotal features extracted from the deep networks are ensembled and subsequently, the machine learning algorithms are used to identify the images based on the extracted features. The said model underwent k-fold cross-validation and achieved an accuracy of 87.90% and 99.10% with an AUC of 0.94 and 1 respectively in identifying TB infected images from Normal and COVID infected images. Also, the model's error rate, F-score, and youden's index values of 0.0093, 0.9901, and 0.9812 for TB versus COVID identification along with the model's accuracy claim that its use can be beneficial in identifying TB infections amid this COVID-19 pandemic, predominantly in countries with limited resources.INDEX TERMS COVID-19, chest X-ray, deep convolutional networks, ensemble learning, machine learning, tuberculosis.The associate editor coordinating the review of this manuscript and approving it for publication was Zhipeng Cai .

show abstract

Section: Related Workmentioning

confidence: 99%

Ensembling of Efficient Deep Convolutional Networks and Machine Learning Algorithms for Resource Effective Detection of Tuberculosis Using Thoracic (Chest) Radiography

Mehrotra¹,

Ansari

Agrawal³

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Ling Ma and Xiabi Liu [9] applied the local binary pattern, the wavelet features, the bag of visual words based on the HOG, and the histogram of CT on a low-level visual scale from the ROI of CISLs. In addition, they used the auto-encoder neural networks for high-level semantic scale with distribution of the optimized features on the midlevel attribute scale.…”

Section: Related Work:-mentioning

confidence: 99%

“…To evaluate this proposed system, we compared the performance of this system with other systems from previous research. The experimental results show the proposed method's accuracy is 92% compared with FCSS [6], MLS [9], FDCT [7], TRIPLET [8], and the method by Gulistan Raj [15]. In figure 9, the average precision of the different methods is rated and compared with the proposed method.The proposed method is more performance than the other methods.…”

Section: Comparison:-mentioning

confidence: 99%

An Intelligent Cbmir System for Detection and Localization of Lung Diseases

Yahia¹,

Al-Dosouki²,

Mokhtar³

et al. 2021

IJAR

View full text Add to dashboard Cite

The diagnosis of lung diseases is a complicated and time-consuming task for radiologists. Often radiologists struggle with accurately diagnosing lung diseases, They use Commonly CT imaging signs (CISs) which common appear in CT lung nodules in the diagnosis of lung diseases. Computer-aided diagnosis systems (CAD) can automatically diagnose and detect these signs by analyzing CT scans, which will reduce radiologists workload. The diagnosis and recognition efficiency and accuracy can be improved by using content-based medical image retrieval (CBMIR). This paper proposes a new intelligent CBMIR method to retrieve CISs helping in diagnosing and recognize lung diseases by using deep Convolutional Neural Network (CNN). Fine-tuned YOLOv4 (You Only Look Once) object detector model are proposed to fast detect and efficiently localize signs in real-time. The proposed CBMIR system can be applied as a useful and accurate medical instrument for diagnostics. The experimental results show an average detection accuracy of CT signs lung diseases as high as 92% and a mean average precision (MAP) of 0.92 is achieved using the proposed technique. Also, it takes only 0.1 milliseconds for the retrieval process. The proposed system presents high improvement as compared to the other system. It achieved better precision of retrieval results and the fastest run of the retrieval time.

show abstract

“…Thus far, research on CBIR has primarily focused on technical capabilities such as retrieval performance. However, studies evaluating the benefit of CBIR systems in a clinical setting are scarce [3][4][5][6][7]. A previous work assessed CBIR applications for diagnosing pulmonary pathologies in chest CTs in an experimental setting, specifically complex pathologies such as diffuse parenchymal lung diseases (DPLDs) [8][9][10]; however, studies adopting a practical approach towards the reading of chest CTs in a realistic setting with a wide variety of possibly rare findings that would demand considerable experience are yet lacking.…”

Section: Introductionmentioning

confidence: 99%

Impact of a content-based image retrieval system on the interpretation of chest CTs of patients with diffuse parenchymal lung disease

et al. 2022

View full text Add to dashboard Cite

Objectives Content-based image retrieval systems (CBIRS) are a new and potentially impactful tool for radiological reporting, but their clinical evaluation is largely missing. This study aimed at assessing the effect of CBIRS on the interpretation of chest CT scans from patients with suspected diffuse parenchymal lung disease (DPLD). Materials and methods A total of 108 retrospectively included chest CT scans with 22 unique, clinically and/or histopathologically verified diagnoses were read by eight radiologists (four residents, four attending, median years reading chest CT scans 2.1± 0.7 and 12 ± 1.8, respectively). The radiologists read and provided the suspected diagnosis at a certified radiological workstation to simulate clinical routine. Half of the readings were done without CBIRS and half with the additional support of the CBIRS. The CBIRS retrieved the most likely of 19 lung-specific patterns from a large database of 6542 thin-section CT scans and provided relevant information (e.g., a list of potential differential diagnoses). Results Reading time decreased by 31.3% (p < 0.001) despite the radiologists searching for additional information more frequently when the CBIRS was available (154 [72%] vs. 95 [43%], p < 0.001). There was a trend towards higher overall diagnostic accuracy (42.2% vs 34.7%, p = 0.083) when the CBIRS was available. Conclusion The use of the CBIRS had a beneficial impact on the reading time of chest CT scans in cases with DPLD. In addition, both resident and attending radiologists were more likely to consult informational resources if they had access to the CBIRS. Further studies are needed to confirm the observed trend towards increased diagnostic accuracy with the use of a CBIRS in practice. Key Points • A content-based image retrieval system for supporting the diagnostic process of reading chest CT scans can decrease reading time by 31.3% (p < 0.001). • The decrease in reading time was present despite frequent usage of the content-based image retrieval system. • Additionally, a trend towards higher diagnostic accuracy was observed when using the content-based image retrieval system (42.2% vs 34.7%, p = 0.083).

show abstract

A multi-level similarity measure for the retrieval of the common CT imaging signs of lung diseases

Cited by 9 publications

References 49 publications

Ensembling of Efficient Deep Convolutional Networks and Machine Learning Algorithms for Resource Effective Detection of Tuberculosis Using Thoracic (Chest) Radiography

Ensembling of Efficient Deep Convolutional Networks and Machine Learning Algorithms for Resource Effective Detection of Tuberculosis Using Thoracic (Chest) Radiography

An Intelligent Cbmir System for Detection and Localization of Lung Diseases

Impact of a content-based image retrieval system on the interpretation of chest CTs of patients with diffuse parenchymal lung disease

Contact Info

Product

Resources

About