EARLY DETECTION OF BRAIN TUMOR USING MRI IMAGES

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, 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
tumor and no tumor. The architecture uses batch normalization for fast training with
a higher learning rate and ease initialization of the layer weights. The proposed
architecture is computationally light model with a small number of convolutional,
max-pooling layers and training iterations. An outstanding competitive accuracy is
achieved of 97.72% in detecting the tumor images when tested on a dataset with 300
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.