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Smart IoT in Breast Cancer Detection Using Optimal Deep Learning.

Ramachandro Majji1, Om Prakash P G2, R Rajeswari3

  • 1Department of Information Technology, Vardhaman College of Engineering, Kacharam, Hyderabad, Telangana, India. rama00565@gmail.com.

Journal of Digital Imaging
|May 23, 2023
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Summary
This summary is machine-generated.

This study introduces a novel Internet of Things (IoT) system for breast cancer classification. The Feedback Artificial Crow Search (FACS)-based Shepherd Convolutional Neural Network (ShCNN) achieves high accuracy in detecting breast cancer from mammograms.

Keywords:
And Shepherd Convolutional Neural NetworkBreast cancerCrow Search AlgorithmFeedback Artificial TreeInternet of Things

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Medical Imaging Analysis

Background:

  • The integration of the Internet of Things (IoT) into healthcare systems offers significant potential for enhancing e-healthcare services.
  • Accurate and timely breast cancer classification is crucial for effective patient treatment and outcomes.
  • Existing methods may face challenges in efficiency and accuracy for complex medical data analysis.

Purpose of the Study:

  • To develop a trustworthy breast cancer classification method using an IoT-based smart healthcare system.
  • To propose a novel Feedback Artificial Crow Search (FACS)-based Shepherd Convolutional Neural Network (ShCNN) for improved classification accuracy.
  • To optimize secure routing operations within the IoT healthcare system using the FACS algorithm.

Main Methods:

  • Implementation of a secure routing protocol using the FACS algorithm, considering factors like distance, energy, link quality, and latency.
  • Feature extraction from pre-processed mammography images, including GLCM and LGBP features.
  • Classification of breast cancer using the ShCNN model, enhanced by the FACS algorithm and data augmentation techniques.

Main Results:

  • The FACS-based ShCNN achieved high performance metrics: 91.56% accuracy, 96.10% sensitivity, 91.80% specificity, and 99.45% True Positive Rate (TPR).
  • The system demonstrated efficient routing with a maximum energy consumption of 0.562 J and a minimum delay of 0.452 s.
  • Extracted features included area, mean, variance, energy, contrast, correlation, skewness, and homogeneity.

Conclusions:

  • The proposed FACS-based ShCNN model offers a reliable and efficient solution for breast cancer classification within IoT healthcare systems.
  • The integration of FACS for routing and ShCNN for classification significantly enhances the performance of smart healthcare applications.
  • This research contributes to advancing AI-driven diagnostic tools for early and accurate detection of breast cancer.