Hybrid intelligent techniques for MRI brain images classification

By El-Sayed A. El-Dahshan, Tamer Hosny, Abdel-Badeeh M. Salem. Digit. Signal Process., 2010

598 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Neurology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bd5d67bc40ec7ee6bad6c6ca045028533bf6647c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Hossam H. Sultan
2. Nancy M. Salem
3. Walid Al-Atabany
4. El-Sayed A. El-Dahshan
5. Luca Saba
6. Mainak Biswas
7. Jasjit S. Suri
8. John R. Laird

In Neurology

1. Hossam H. Sultan
2. Nancy M. Salem
3. Walid Al-Atabany

In Signal Processing

1. Hossam H. Sultan
2. Nancy M. Salem
3. Walid Al-Atabany
4. Jasjit S. Suri

Citations in the corpus

• Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm (2014, methodology): “Other supervised classification techniques, such as k-NN can be used to classify the normal/pathological T2-weighted MRI images (El-Dahshan et al., 2010).”
• Multi-Classification of Brain Tumor Images Using Deep Neural Network (2019, methodology): “[22] introduced a method to classify 80 brain tumor normal and abnormal images using Discrete Wavelet Transform (DWT) to extract features, Principal Component Analysis (PCA) to reduce features, and then ANN and KNN to classify images with overall accuracy of 97% and 98% respectively.”
• A Review on a Deep Learning Perspective in Brain Cancer Classification (2019, methodology): “Hybrid characterization system for brain cancer characterization [88].”