An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases

Kashif, Muhammad; Raja, Gulistan; Shaukat, Furqan

doi:10.1007/s10278-020-00338-w

Cited by 25 publications

(25 citation statements)

References 60 publications

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“…Recently, medical imaging comprises of dissimilar imaging modalities such as ultrasound, fluoroscopy, computed tomography, and histopathology that helps in interpreting and understanding the dissimilar organs of the human body [ 2 , 3 ]. In recent scenario, the medical facilities and hospitals create an enormous number of medical images, where it is a complex task to interpret medical images that needs extensive knowledge [ 4 , 5 ]. So, researchers developed many support systems such as computer aided diagnosis system and content-based medical image retrieval (CBMIR), to assist radiologists or clinicians for interpreting the medical images [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Praveena¹,

Guptha²,

Kazemzadeh

et al. 2022

Journal of Healthcare Engineering

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In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the large medical databases. So, it is essential in designing an effective automated system for medical image retrieval. In this research study, the input medical images are acquired from new Pap smear dataset, and then, the visible quality of acquired medical images is improved by applying image normalization technique. Furthermore, the hybrid feature extraction is accomplished using histogram of oriented gradients and modified local binary pattern to extract the color and texture feature vectors that significantly reduces the semantic gap between the feature vectors. The obtained feature vectors are fed to the independent condensed nearest neighbor classifier to classify the seven classes of cell images. Finally, relevant medical images are retrieved using chi square distance measure. Simulation results confirmed that the proposed model obtained effective performance in image retrieval in light of specificity, recall, precision, accuracy, and f-score. The proposed model almost achieved 98.88% of retrieval accuracy, which is better compared to other deep learning models such as long short-term memory network, deep neural network, and convolutional neural network.

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Section: Introductionmentioning

confidence: 99%

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Praveena¹,

Guptha²,

Kazemzadeh

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Kashif, Gulistan Raj. and f. Shaukat [15] applied a sum of descriptors such as local ternary pattern, local phase quantization, and discrete wavelet transform with joint mutual information for feature selection which apply the same method of [6].…”

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

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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.

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“…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

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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).

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An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases

Cited by 25 publications

References 60 publications

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model

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

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