An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification

@inproceedings{Siddiqui2015AnAA,
  title={An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification},
  author={Muhammad Faisal Siddiqui and Ahmed Wasif Reza and Jeevan Kanesan and Gajendra P. S. Raghava},
  booktitle={PloS one},
  year={2015}
}
A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares… CONTINUE READING
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Key Quantitative Results

  • From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively.
  • From the analy- sis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computa- tion time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively.
  • The proposed system correctly classified the MR images of Group-1 and Group2 with an average area under curve (AUC) of 100%, with 0% standard deviation.
  • By using these optimized values of the parameters, we achieved 100% accuracy while testing different benchmark dataset groups and made our system a generalized one.

Citations

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MR-MS Image Classification based on Convolutional Neural Networks

  • 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT)
  • 2019

Classification of single-channel EEG signals for epileptic seizures detection based on hybrid features

  • Technology and health care : official journal of the European Society for Engineering and Medicine
  • 2018
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