ArticleJournal of medical systems2019
Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 62 citations in OpenAlex.
- Artificial intelligence and its potential integration with the clinical practice of diagnostic imaging medical physicists: a review.Physical and engineering sciences in medicine · 2025Review
- Faster R-CNN model for target recognition and diagnosis of scapular fractures.Journal of bone oncology · 2025Article
- Convolutional Fine-Tuned Threshold Adaboost approach for effectual content-based image retrieval.Scientific reports · 2025Article
- Effective and efficient content-based similarity retrieval of large lung CT images based on WSSLN model.PloS one · 2023Article
- Detection of Tomato Leaf Miner Using Deep Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models.Sensors (Basel, Switzerland) · 2022Article
- Plant diseases and pests detection based on deep learning: a review.Plant methods · 2021Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Avascular Necrosis (AN) is a cause of muscular-skeletal disability. As it is common amongst the younger people, early intervention and prompt diagnosis is requisite. This disease normally affects the femoral bones, in order that the bones' shape gets altered due to the fracture. Other common sites encompass knees, humerus, shoulders, jaw, and ankles. The retrieval of the AN affected bone images is challenging due to its varied fracture locations. This work proposes an effectual methodology for retrieval of AN images utilizing Deep Belief CNN Feature Representation. Initially, the input dataset undergoes preprocessing. The image noise is eradicated utilizing Median Filter (MF) and is resized in the preprocessing stage. Features are represented using Deep Belief Convolutional Neural Network (DB-CNN). Now, the image feature representations are transmuted to binary codes. Then, the similarity measurement is computed utilizing Modified-Hamming Distance. Finally, the images are retrieved centered on the similarity values. The test outcomes evinced that the proposed work is better than the other existent techniques.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.