Multi-Class Disease Classification in Brain MRIs Using a Computer-Aided Diagnostic System

@article{Siddiqui2017MultiClassDC,
  title={Multi-Class Disease Classification in Brain MRIs Using a Computer-Aided Diagnostic System},
  author={Muhammad Faisal Siddiqui and Ghulam Mujtaba Shaikh and Ahmed Wasif Reza and Nor Liyana Mohd Shuib},
  journal={Symmetry},
  year={2017},
  volume={9},
  pages={37}
}
Background: An accurate and automatic computer-aided multi-class decision support system to classify the magnetic resonance imaging (MRI) scans of the human brain as normal, Alzheimer, AIDS, cerebral calcinosis, glioma, or metastatic, which helps the radiologists to diagnose the disease in brain MRIs is created. Methods: The performance of the proposed system is validated by using benchmark MRI datasets (OASIS and Harvard) of 310 patients. Master features of the images are extracted using a… CONTINUE READING

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Key Quantitative Results

  • The RF-based classifier outperformed among all compared decision models and achieved an average accuracy of 96% with 4% standard deviation, and an area under the receiver operating characteristic (ROC) curve of 99%.

References

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