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Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture

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Summary

This study introduces a cloud-based computer-aided diagnosis (CAD) system for early breast cancer detection. The system uses AlexNet, GLCM features, and MK-SVM to achieve 96.26% accuracy, improving accessibility for remote patients.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Early breast cancer detection is crucial for reducing mortality rates.
  • Image-based diagnosis faces challenges due to lesion appearance variability.
  • Accessible breast cancer screening is limited in remote areas.

Purpose of the Study:

  • To develop a cloud-based computer-aided diagnosis (CAD) system for remote breast cancer detection.
  • To enhance diagnostic accuracy through advanced machine learning techniques.
  • To provide accessible breast cancer screening for underserved populations.

Main Methods:

  • A novel CAD system integrating AlexNet architecture and Gray-Level Co-occurrence Matrix (GLCM) features for texture analysis.
  • Utilizing an ensemble of Multiple Kernel Support Vector Machines (MK-SVM) for classification.
  • Testing the model on the publicly available MIAS dataset for breast image analysis.

Main Results:

  • The proposed CAD system achieved a high accuracy of 96.26% in breast cancer classification.
  • The fusion of AlexNet and GLCM features effectively extracted distinguishing texture patterns.
  • The ensemble MK-SVM classifier further refined diagnostic precision.

Conclusions:

  • The developed cloud-based CAD system offers a promising solution for accurate and accessible breast cancer detection.
  • This technology can significantly benefit patients in remote areas lacking medical facilities.
  • The system has the potential to aid in patient monitoring, especially for the elderly and disabled.