Author/Authors :
Tahmasebi Birgani, M. J Department of Radiation Oncology - Faculty of Medicine - Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran , Chegeni, N Department of Medical Physics - Faculty of Medicine - Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran , Farhadi Birgani, F Department of Medical Physics - Faculty of Medicine - Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran , Fatehi, D Department of Medical Physics - Faculty of Medicine - Shahrekord University of Medical Sciences, Shahrekord, Iran , Akbarizadeh, Gh Department of Electrical Engineering - Faculty of Engineering - Shahid Chamran University of Ahvaz, Ahvaz, Iran , Shams, A Department of Radiation Oncology - Golestan Hospital - Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
Abstract :
Background: One of the leading causes of death is brain tumors. Accurate tumor
classification leads to appropriate decision making and providing the most efficient
treatment to the patients. This study aims to optimize brain tumor MR images classification
accuracy using optimal threshold, PCA and training Adaptive Neuro Fuzzy
Inference System (ANFIS) with different repetitions.
Material and Methods: The procedure used in this study consists of five
steps: (1) T1, T2 weighted images collection, (2) tumor separation with different
threshold levels, (3) feature extraction, (4) presence and absence of feature reduction
applying principal component analysis (PCA) and (5) ANFIS classification with 0, 20
and 200 training repetitions.
Results: ANFIS accuracy was 40%, 80% and 97% for all features and 97%,
98.5% and 100% for the 6 selected features by PCA in 0, 20 and 200 training repetitions,
respectively.
Conclusion: The findings of the present study demonstrated that accuracy can
be raised up to 100% by using an optimized threshold method, PCA and increasing
training repetitions.
Keywords :
Training Repetition , PCA , ANFIS , Brain Tumor Detection , MRI
