TY - JOUR ID - 88213 TI - Automatic Brain MRI Slices Classification Using Hybrid Technique JO - Al-Rafidain Engineering Journal (AREJ) JA - AREJ LA - en SN - 1813-0526 AU - AhlamFadhil Mahmood, Dr. AU - Mohammed Abd-Alsalam, Ameen AD - Y1 - 2014 PY - 2014 VL - 22 IS - 3 SP - 198 EP - 212 KW - KEYWORDS KW - Fuzzy Inference System KW - Feed Forward Neural Network KW - MRI DO - 10.33899/rengj.2014.88213 N2 - Abstract This paper presents an intelligent classification technique to identify normal and abnormal slices of the magnetic resonance human brain images(MRI). The prtoposed hybrid technique consists of four subsequent stages; namely, dimensionality reduction, preprocessing, feature extraction, and classification. In the initial stages, the enhancement and removed unwanted informationare applied to provide a more appropriate image for the subsequent automated stages. In feature extraction stage, the most efficient features like statistical, and Haar wavelet features are extracted from each slice of brain MR images. In the classification stage, initially performs classification process by utilizing Fuzzy Inference System (FIS) and secondly Feed Forward Neural Network (FFNN) is used to classify the braintissue to normal or abnormal. The proposed automated system is tested on a data set of 572 MRI images using T1 horizontal transverse (axial) section of the brain. Hybrid method yields high sensitivity of 100%, specificity of 100% and overallaccuracy of 95.66% over FIS and FFNN. The classification result shows that the proposed hybrid techniques are robust and effective compared with other recently work. Keywords: Brain Tumor Classification; Fuzzy Inference System; Feed Forward Neural Network; MRI . UR - https://rengj.mosuljournals.com/article_88213.html L1 - https://rengj.mosuljournals.com/article_88213_8ad087fb82f58e7ee62aa82b2587935e.pdf ER -