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

arXiv: https://arxiv.org/abs/2406.18549 · HTML: https://mlsanity.com/paper/2406.18549/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning](https://mlsanity.com/paper/2112.04489/) (2021)
- [Survival Prediction Across Diverse Cancer Types Using Neural Networks](https://mlsanity.com/paper/2404.08713/) (2024)
- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example](https://mlsanity.com/paper/2404.08279/) (2024)
- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [Regional Style and Color Transfer](https://mlsanity.com/paper/2404.13880/) (2024)
- [The FacT: Taming Latent Factor Models for Explainability with Factorization Trees](https://mlsanity.com/paper/1906.02037/) (2019)
- [Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates](https://mlsanity.com/paper/2310.19807/) (2023)
- [Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis](https://mlsanity.com/paper/2404.08003/) (2024)
- [Constrained Attractor Selection Using Deep Reinforcement Learning](https://mlsanity.com/paper/1909.10500/) (2019)
- [Recurrent Neural Network from Adder's Perspective: Carry-lookahead RNN](https://mlsanity.com/paper/2106.12901/) (2021)
- [Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification](https://mlsanity.com/paper/2404.14606/) (2024)
- [Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning](https://mlsanity.com/paper/2010.01255/) (2020)
- [Utilizing Deep Learning to Optimize Software Development Processes](https://mlsanity.com/paper/2404.13630/) (2024)

## Built on by (most cited first, 7 of 7)

- [Attention Mechanism and Context Modeling System for Text Mining Machine Translation](https://mlsanity.com/paper/2408.04216/) (2024)
- [Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits](https://mlsanity.com/paper/2408.07084/) (2024)
- [A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images](https://mlsanity.com/paper/2409.15332/) (2024)
- [Convolutional Neural Networks for Predictive Modeling of Lung Disease](https://mlsanity.com/paper/2408.12605/) (2024)
- [Reducing Bias in Deep Learning Optimization: The RSGDM Approach](https://mlsanity.com/paper/2409.15314/) (2024)
- [Stock Type Prediction Model Based on Hierarchical Graph Neural Network](https://mlsanity.com/paper/2412.06862/) (2024)
- [RaX-Crash: A Resource Efficient and Explainable Small Model Pipeline with an Application to City Scale Injury Severity Prediction](https://mlsanity.com/paper/2512.07848/) (2025)

## 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.