Intelligence Quotient Classification from Human MRI Brain Images Using Convolutional Neural Network

2020 ◽  
Author(s):  
A Arya ◽  
Manju Manuel
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Intelligence Quotient ◽  
Brain Images ◽  
Mri Brain ◽  
Mri Brain Images

scholarly journals MRI Brain Abnormality Detection Using Conventional Neural Network (CNN)

2021 ◽  
Author(s):  
Jeevitha R ◽  
Selvaraj D
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Brain Tumours ◽  
Input Image ◽  
Brain Images ◽  
Fuzzy C Means ◽  
Brain Abnormality ◽  
Mri Brain ◽  
Skull Stripping ◽  
Fuzzy C Means Algorithm

Brain tumours has huge heterogeneity and there is always a familiarity between normal and abnormal tissues and hence the extraction of tumour portions from normal images becomes persistent. In this paper, MRI brain tumor detection is performed from a brain images using Fuzzy C-means(FCM) algorithm and sebsequently Convolutional Neural Network(CNN) algorithm is employed. Here, firstly preprocessing step is performed by Skull Stripping algorithm followed by Segmentation process. Fuzzy C-means algorithm is used to segment the Cerebrospinal Fluid(CSF), Grey matter(GM) and White Matter(WM) from the database. The third part is to extract features to find whether the tumor is present or not, here eleven features are extracted like mean, entropy, S.D(Standard Deviation). The final part is the classification process done by Convolutional Neural Network(CNN) in which it is able to differentiate whether the input image is normal image or an abnormal image. Compared to other methods, here the values of the features extracted are higher for normal images than for abnormal Images and it is shown from the graphs drawn from the extracted features.

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