Abstract
Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.
Highlights
The brain is the most complex organ present in the human body
We are going to classify the brain Magnetic Resonance Imaging (MRI) images into normal or abnormal based on a specific range of ages, as it is already established by Brown [12] that the structure of the brain varies according to age
In most cases, employing a cross-fold validation and generalization strategy, LIM and Convolutional Neural Network (CNN)-Deep Neural Network (DNN) produce better results than Support Vector Machine (SVM) and AlexNet when dealing with heterogeneous data
Summary
The brain is the most complex organ present in the human body. It carries out different functions and controls the activities of other systems of the body. MRI, including tumors, cysts, and other structural abnormalities It can detect gray matter, white matter, and any damage or shunt present in the brain. Keeping the necessity of manual examination, this paper includes state-of-the-art automated approaches to classify MRI images as normal (nontumorous) or abnormal (tumorous). For this purpose, a proposed deep learning based CNN methodology was used and compared with the existing techniques due to their superior performance in Computer. Based on available data obtained, the images are divided into seven categories based on different age groups and gender These are classified using proposed CNN models where output can be normal or abnormal. This is applied to various approaches for classification as normal or abnormal
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