ArticleBioMed research international2020
Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Development and validation of an interpretable nomogram for predicting the risk of the prolonged postoperative length of stay for tuberculous spondylitis: a novel approach for risk stratification.BMC musculoskeletal disorders · 2025Article
- Instance-level medical image classification for text-based retrieval in a medical data integration center.Computational and structural biotechnology journal · 2024Article
- Analysis of Gait Characteristics of Patients with Knee Arthritis Based on Human Posture Estimation.BioMed research international · 2022Article
- Pulse Signal Analysis Based on Deep Learning Network.BioMed research international · 2022Article
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Authors and funding
3 authors.
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Abstract
Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in CBMIR systems. Features play a vital role in the accuracy and speed of the search process. In this paper, we propose a deep convolutional neural network- (CNN-) based framework to learn concise feature vector for medical image retrieval. The medical images are decomposed into five components using empirical mode decomposition (EMD). The deep CNN is trained in a supervised way with multicomponent input, and the learned features are used to retrieve medical images. The IRMA dataset, containing 11,000 X-ray images, 116 classes, is used to validate the proposed method. We achieve a total IRMA error of 43.21 and a mean average precision of 0.86 for retrieval task and IRMA error of 68.48 and F1 measure of 0.66 on classification task, which is the best result compared with existing literature for this dataset.
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