mlsanityData to 7 Oct 2026

Advancements in Feature Extraction Recognition of Medical Imaging Systems Through Deep Learning Technique

arXiv 2406.18549 ↗First posted 23 May 2024eess.IV cs.CVcited by 7

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Abstract (arXiv)

This study introduces a novel unsupervised medical image feature extraction method that employs spatial stratification techniques. An objective function based on weight is proposed to achieve the purpose of fast image recognition. The algorithm divides the pixels of the image into multiple subdomains and uses a quadtree to access the image. A technique for threshold optimization utilizing a simplex algorithm is presented. Aiming at the nonlinear characteristics of hyperspectral images, a generalized discriminant analysis algorithm based on kernel function is proposed. In this project, a hyperspectral remote sensing image is taken as the object, and we investigate its mathematical modeling, solution methods, and feature extraction techniques. It is found that different types of objects are independent of each other and compact in image processing. Compared with the traditional linear discrimination method, the result of image segmentation is better. This method can not only overcome the disadvantage of the traditional method which is easy to be affected by light, but also extract the features of the object quickly and accurately. It has important reference significance for clinical diagnosis.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 23 May 2024.

Builds on

14 of 28 references.

References · 14

  1. 2021Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learningcited by 125
  2. 2024Survival Prediction Across Diverse Cancer Types Using Neural Networkscited by 80
  3. 2023Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networkscited by 54
  4. 2024Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an examplecited by 50
  5. 2024Exploring Diverse Methods in Visual Question Answeringcited by 45
  6. 2024Regional Style and Color Transfercited by 30
  7. 2019The FacT: Taming Latent Factor Models for Explainability with Factorization Treescited by 27
  8. 2024Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysiscited by 21
  9. 2023Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updatescited by 20
  10. 2019Constrained Attractor Selection Using Deep Reinforcement Learningcited by 17
  11. 2021Recurrent Neural Network from Adder's Perspective: Carry-lookahead RNNcited by 16
  12. 2024Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classificationcited by 16
2 more
  1. 2020Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learningcited by 15
  2. 2024Utilizing Deep Learning to Optimize Software Development Processescited by 15

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Cited by year

Citing papers per year: 2024: 6; 2025: 1.

7 papers.

  1. 2024Attention Mechanism and Context Modeling System for Text Mining Machine Translationcited by 15
  2. 2024Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visitscited by 12
  3. 2024A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Imagescited by 11
  4. 2024Convolutional Neural Networks for Predictive Modeling of Lung Diseasecited by 7
  5. 2024Reducing Bias in Deep Learning Optimization: The RSGDM Approachcited by 7
  6. 2024Stock Type Prediction Model Based on Hierarchical Graph Neural Networkcited by 5
  7. 2025RaX-Crash: A Resource Efficient and Explainable Small Model Pipeline with an Application to City Scale Injury Severity Predictioncited by 0