ArticleMicromachines2022
Deep Learning Based Feature Selection Algorithm for Small Targets Based on mRMR.
Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
The trial behind it
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
9 citing papers in PubMed.
- Muscle MR Radiomics for Evaluation of Idiopathic Inflammatory MyopathiesCurrent medical imaging · 2026Article
- The Integration of Artificial Intelligence with Micro-Nano-Systems: Perspectives, Challenges and Future Prospects.Micromachines · 2025Article
- Comparison of deep learning methods for the radiographic detection of patients with different periodontitis stages.Dento maxillo facial radiology · 2024Article
- Energy optimization for wireless sensor network using minimum redundancy maximum relevance feature selection and classification techniques.PeerJ. Computer science · 2024Article
- Multi-omics reveal microbial determinants impacting the treatment outcome of antidepressants in major depressive disorder.Microbiome · 2023Article
- A Deep Learning Approach for Predicting Multiple Sclerosis.Micromachines · 2023Article
- Research on Automatic Identification and Rating of Ferrite-Pearlite Grain Boundaries Based on Deep Learning.Materials (Basel, Switzerland) · 2023Article
- Research on Intelligent Identification and Grading of Nonmetallic Inclusions in Steels Based on Deep Learning.Micromachines · 2023Article
- Hardness-and-Type Recognition of Different Objects Based on a Novel Porous Graphene Flexible Tactile Sensor Array.Micromachines · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Small target features are difficult to distinguish and identify in an environment with complex backgrounds. The identification and extraction of multi-dimensional features have been realized due to the rapid development of deep learning, but there are still redundant relationships between features, reducing feature recognition accuracy. The YOLOv5 neural network is used in this paper to achieve preliminary feature extraction, and the minimum redundancy maximum relevance algorithm is used for the 512 candidate features extracted in the fully connected layer to perform de-redundancy processing on the features with high correlation, reducing the dimension of the feature set and making small target feature recognition a reality. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can be improved. Simultaneously, by pre-processing the image, the feature recognition of the pre-processed image can significantly improve the recognition accuracy. The experimental results demonstrate that using the minimum redundancy maximum relevance algorithm can effectively reduce the feature dimension and identify small target features.
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What OpenQuestion holds
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.