@inproceedings{e0e0e41f4f4743a6a16d0f755d937d15,
title = "Brain MRI classification using discrete wavelet transform and bag-of-words",
abstract = "Automatic CAD system able to detect correctly the unhealthy brain in magnetic resonance imaging (MRI) scanning is represented in this paper. The new system exploited Discrete Wavelet Transform (DWT) and Bag-of-Words (BoW) to extract image features. Support vector machine (SVM) was used in classification step. We employed 256×256 images from three datasets (DS-66, DS-160, DS-255) provided by Harvard Medical School, to evaluate our method. 10∗k-fold stratified Cross Validation (CV) technique was applied to validate the system performance. The Accuracy reached respectively 100\%, 100\%, and 99.61\% for DS-66, DS-160, and DS-255 datasets. The overall computation time is about 0.027 s for each MR image. A comparative study with several works showed efficiency and robustness of our scheme.",
keywords = "BoW, Brain Tumor, CAD, Classification, DWT, MRI",
author = "Wadhah Ayadi and Wajdi Elhamzi and Imen Charfi and Bouraoui Ouni and Mohamed Atri",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 13th International Conference on Advanced Systems and Electric Technologies, IC\_ASET 2018 ; Conference date: 22-03-2018 Through 25-03-2018",
year = "2018",
month = jun,
day = "11",
doi = "10.1109/ASET.2018.8379832",
language = "English",
series = "2018 International Conference on Advanced Systems and Electric Technologies, IC\_ASET 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "45--49",
editor = "Naoufel Machta and \{Ben Amor\}, Abdessattar and Salwa Elloumi",
booktitle = "2018 International Conference on Advanced Systems and Electric Technologies, IC\_ASET 2018",
address = "United States",
}