Abstract
Brain diseases are mainly caused by abnormal growth of brain cells that may damage the brain structure, and eventually will lead to malignant brain cancer. An early diagnosis to enable decisive treatment using a Computer-Aided Diagnosis (CAD) system has major challenges, especially accurate detection of different diseases in the magnetic resonance imaging (MRI) images. In this paper, a three-step preprocessing is proposed to enhance the quality of MRI images, along with a new Deep Convolutional Neural Network (DCNN) architecture for effective diagnosis of glioma, meningioma, and pituitary. The architecture uses batch normalization for fast training with a higher learning rate and ease initialization of the layer weights. The proposed architecture is a computationally lightweight model with a small number of convolutional, max-pooling layers and training iterations. A demonstrative comparison between the proposed architecture and other discussed models in this paper is conducted. An outstanding competitive accuracy is achieved of 98.22% overall, 99% in detecting glioma, 99.13% in detecting meningioma, 97.3% in detecting pituitary and 97.14% in detecting normal images when tested on a dataset with 3394 MRI images. Experimental results prove the robustness of the proposed architecture which has increased the detection accuracy of a variety of brain diseases in a short time.
Highlights
The human brain is the most important part of the body because it controls most of human actions such as memory, speech, thoughts and leg and arms movements [1]
We hired the test part of the used dataset to assess the result of the explored models (VGG16, VGG19, convolutional neural network (CNN)-Support Vector Machine (SVM), and the proposed model)
A deep convolution neural network architecture is proposed for glioma, meningioma and pituitary brain diseases detection with an objective of high classification accuracy within a short time. first, a proper brain tumor dataset for efficiently performing the training and testing process
Summary
The human brain is the most important part of the body because it controls most of human actions such as memory, speech, thoughts and leg and arms movements [1]. According to the world health organization (WHO) records, about 9.6 million on every side of the world died from cancer in 2018 [3]. The MRI images can provide better visualization of contrast and spatial definition [4]. The detection of brain abnormalities process is an important issue to determine whether the abnormalities exist or not in MRI images. Researchers uses deep learning in a wide zone with many medical science fields [5].
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