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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Related Experiment Video

Updated: Jun 9, 2025

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
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Secure and Transparent Lung and Colon Cancer Classification Using Blockchain and Microsoft Azure.

Entesar Hamed I Eliwa1,2, Amr Mohamed El Koshiry3,4, Tarek Abd El-Hafeez2,5

  • 1Department of Mathematics and Statistics, College of Science, King Faisal University, P.O. Box 400, Al-Ahsa 31982, Saudi Arabia.

Advances in Respiratory Medicine
|October 25, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a secure framework using blockchain and Azure for remote cancer diagnosis. It achieves 100% accuracy in classifying lung and colon cancers, enhancing patient care.

Keywords:
Microsoft Azureblockchain technologycloud servicescolon cancerconvolutional neural networks (CNN)lung cancerreal-time diagnosissecure remote consultations

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Area of Science:

  • Oncology
  • Computer Science
  • Health Informatics

Background:

  • Global healthcare systems face diagnostic and management challenges for lung and colon cancers.
  • Traditional diagnostic methods are inefficient and raise data privacy concerns.

Purpose of the Study:

  • To develop a secure and transparent framework for remote cancer diagnosis using blockchain and Microsoft Azure.
  • To leverage advanced machine learning models for accurate classification of lung and colon cancers.

Main Methods:

  • Integration of Microsoft Azure cloud services with a permissioned blockchain network.
  • Secure data handling including anonymization, encryption, and controlled access via smart contracts.
  • Utilizing Azure Machine Learning for training and deploying models on the LC25000 histopathological image dataset.

Main Results:

  • Achieved 100% accuracy in lung and colon cancer classification using DenseNet, ResNet50, and MobileNet models.
  • Demonstrated exceptional performance with F1-scores and cross-validation accuracies exceeding 99.9%.
  • Enhanced diagnostic process efficiency and transparency through real-time notifications and secure remote consultations.

Conclusions:

  • The proposed framework effectively enhances diagnostic accuracy and data security for lung and colon cancer.
  • Blockchain and Azure integration offers a robust solution for secure remote consultations and cancer care management.
  • The study highlights the potential of advanced technologies in improving patient outcomes and streamlining healthcare processes.