Modeling clinician medical-knowledge in terms of med-level features for semantic content-based mammogram retrieval

Baâzaoui, Abir; Barhoumi, Walid; Ahmed, Amr; Zagrouba, Ezzeddine

doi:10.1016/j.eswa.2017.10.034

Cited by 13 publications

(3 citation statements)

References 15 publications

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“…Content-based image retrieval (CBIR) techniques have risen in popularity in recent years, and they are now commonly used in the automatic diagnosis of disease from MR images, mammograms (5), and other sources. It requires the image dataset in order to find the query image from the image dataset, which is based on the visual content of the photos.…”

Section: Related Workmentioning

confidence: 99%

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Bypassing Confines of Feature Extraction in Brain Tumor Retrieval via MR Images by CBIR

Saraswat

Sharma

2022

ECS Trans.

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To check out the health of the patient, digital images are generated every single day and are used by the radiologist for extracting out the details and anomalies. The complicated part is to figure out the disease in those images. By the manual diagnosis of the images through the radiologists, the doctors can get to know exact scenario of the abnormalities in images, but is considerably more difficult with Content Based Image Retrieval (CBIR) to get those finer details from MR images. These CBIR approaches are now frequently employed in the automatic diagnosis of disease from MR images, mammograms, and other sources. Bridging this gap can be done with deep learning feature extraction algorithm and the canny edge detection technique we propose, and accuracy closer to the manual results of a human evaluator can be achieved to a significant extent as part of the goal of sustainable development through innovation.

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Section: Related Workmentioning

confidence: 99%

“…According to (18), for a given query, a search space has been created in which all the photos that are similar in terms of their features are saved, and this is utilised to identify the similarity-based search strategy. For the two-step search space, local patterns are being used.…”

Section: Related Workmentioning

confidence: 99%

Bypassing Confines of Feature Extraction in Brain Tumor Retrieval via MR Images by CBIR

Saraswat

Sharma

2022

ECS Trans.

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“…Kumar and Singh proposed the building of a seeking system, using the combination of SIFT, Hu moment, and GLCM algorithm, mapped with the Euclidean method distance. The work of Baazaoui et al deals with the proposition of a searching task, in a mammographic context, starting from a low‐level analysis in coordination with a conceptual source. Table provides a summary of different methods in the medical field, helping to build the critical point of view.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Medical social network content analysis for medical image retrieval purpose

Ayadi¹,

Bouslimi²,

Akaichi

2019

Softw Pract Exp

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Background and objective: Medical social networking platforms provide virtual spaces ensuring the interaction between different healthcare participants. As a part of the exchange, these spaces allow subscribers to upload medical images, describing different medical cases for an analysis or an interpretation proposal. Facing this expected huge amount of uploaded images generated daily, it is needed to engage new mechanisms to effectively deal with this circumstance, for enhancing the search function process of medical images, based on what is uploaded. To overcome this issue, setting up of images visual searching based on a content-based medical image retrieval scheme is the solution.More clearly, such mechanism will help and motivate medical social networking subscribers to find visually similar stored images.Methods: To ensure this task, the development of this mechanism, technically, is based mainly on a fusion of three visual features, which offers a flexible and more precision. It is reinforced by a weighted distance approach through attributing weights for feature vectors to scale up the performance. Indeed, the displayed results of this system can be updated based on user's intention by a user interactive feedback mechanism to indicate the truly relevant images. Results: We provide the theoretical performance of our scheme. Extensive experiments were conducted on a categorically classified collection containing 500 images. We conduct a practical evaluation on this dataset classes, putting returned results in a comparative study with other models results, existing in the literature. Conclusions:The proposed scheme preserves the efficiency of the search task. As theoretically and experimentally established, our scheme offers an effective image retrieval model that can support different subscribers' expectations.The relevance feedback mechanism can keep the dynamism of the system, thus offering a continuous searching result evolution. Experimentation outcomes indicate better findings compared with the other models.

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Optimization and Deep Learning–Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images

Suresh¹,

Sundaresan²,

Raghavan³

et al. 2021

Computational Analysis and Deep Learning for Medical Care

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Modeling clinician medical-knowledge in terms of med-level features for semantic content-based mammogram retrieval

Cited by 13 publications

References 15 publications

Bypassing Confines of Feature Extraction in Brain Tumor Retrieval via MR Images by CBIR

Bypassing Confines of Feature Extraction in Brain Tumor Retrieval via MR Images by CBIR

Medical social network content analysis for medical image retrieval purpose

Optimization and Deep Learning–Based Content Retrieval, Indexing, and Metric Learning Approach for Medical Images

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