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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

A big data analysis algorithm for massive sensor medical images.

Sarah A Alzakari¹, Nuha Alruwais², Shaymaa Sorour³

¹Department of Computer Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Peerj. Computer Science

|December 9, 2024

Summary

This summary is machine-generated.

This study introduces a machine learning-based anomaly detection system for medical images to enhance patient care. The system addresses limitations of current methods, improving timely diagnosis and treatment through efficient data processing and feature selection.

Keywords:

Anomaly detection Big data analysis Feature extraction Healthcare Monitoring Sensor images

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Area of Science:

Medical Informatics
Machine Learning
Big Data Analytics

Background:

Big data analytics transforms healthcare, enabling evidence-based clinical decisions.
Smart sensor systems in healthcare raise significant privacy and security concerns for sensitive medical data.
Existing anomaly detection methods in healthcare face challenges like high resource use, poor feature selection, and ineffective temporal data handling.

Purpose of the Study:

To develop an improved anomaly detection system for medical images using machine learning.
To enhance patient care and well-being through timely notifications and treatments.
To overcome the limitations of current anomaly detection techniques in terms of resource consumption, feature selection, and real-time monitoring.

Main Methods:

Data preprocessing, including transfer, imputation of missing values, and sanitization.
Feature selection and extraction using Recursive Feature Elimination (RFE) and Dynamic Principal Component Analysis (DPCA).
Anomaly identification employing an Auto-encoded Genetic Recurrent Neural Network (AGRNN) approach.

Main Results:

The proposed system aims to provide accurate anomaly detection with low latency.
Evaluation metrics include data arrival rate, resource consumption, propagation delay, transaction epoch, true positive rate, false alarm rate, and Root Mean Square Error (RMSE).
The study anticipates improved patient data anomaly detection and timely intervention.

Conclusions:

The developed machine learning system offers a promising approach to anomaly detection in medical images.
Addressing privacy and security concerns is crucial for big data analytics in healthcare.
The proposed methods aim to enhance the efficiency and accuracy of anomaly detection for critical healthcare applications.