Tahmasebi Birgani, Mohamad Javad, Farhadi Birgani, Fariba, Chegeni, Nahid. (1397). Optimization of the brain tumor MR images classification accuracy using the optimal threshold, PCA and training ANFIS with different repetitions. , 15(Special Issue-12th. Iranian Congress of Medical Physics), 33-33. doi: 10.22038/ijmp.2018.11888
Mohamad Javad Tahmasebi Birgani; Fariba Farhadi Birgani; Nahid Chegeni. "Optimization of the brain tumor MR images classification accuracy using the optimal threshold, PCA and training ANFIS with different repetitions". , 15, Special Issue-12th. Iranian Congress of Medical Physics, 1397, 33-33. doi: 10.22038/ijmp.2018.11888
Tahmasebi Birgani, Mohamad Javad, Farhadi Birgani, Fariba, Chegeni, Nahid. (1397). 'Optimization of the brain tumor MR images classification accuracy using the optimal threshold, PCA and training ANFIS with different repetitions', , 15(Special Issue-12th. Iranian Congress of Medical Physics), pp. 33-33. doi: 10.22038/ijmp.2018.11888
Tahmasebi Birgani, Mohamad Javad, Farhadi Birgani, Fariba, Chegeni, Nahid. Optimization of the brain tumor MR images classification accuracy using the optimal threshold, PCA and training ANFIS with different repetitions. , 1397; 15(Special Issue-12th. Iranian Congress of Medical Physics): 33-33. doi: 10.22038/ijmp.2018.11888

Optimization of the brain tumor MR images classification accuracy using the optimal threshold, PCA and training ANFIS with different repetitions

Iranian Journal of Medical Physics
مقاله 33، دوره 15، Special Issue-12th. Iranian Congress of Medical Physics، بهمن و اسفند 2018، صفحه 33-33
نوع مقاله: Conference Proceedings
شناسه دیجیتال (DOI): 10.22038/ijmp.2018.11888
نویسندگان
Mohamad Javad Tahmasebi Birgani1؛ Fariba Farhadi Birgani* 2؛ Nahid Chegeni2
1Radiation Therapy and Medical Physics Department, Golestan Hospital, Jundishapur University of Medical Sciences, Ahvaz, Iran
2Department of Medical Physics, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
چکیده
Introduction: One of the leading causes of death among people 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 of the brain tumor MR images classification accuracy using the optimal threshold, PCA and training Adaptive Neuro Fuzzy Inference System (ANFIS) with different repetitions.
Materials and Methods: This 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 showed that accuracy can be raised up to 100% by using an optimized threshold method, PCA and increasing training repetitions.
کلیدواژه‌ها
MRI؛ Brain Tumor Detection؛ ANFIS؛ PCA؛ Training Repetition
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