Image analysis for MRI based brain tumour detection using hybrid segmentation and deep learning classification technique

Abstract

Indefinite and uncontrollable growth of cells leads to tumours in the human brain. A proper treatment and early diagnosis of brain tumours are essential to prevent permanent damage to the brain. In medical image diagnosis, the tumour segmentation and classification schemes are used for identifying the tumour and non-tumour cells in the brain. The automatic classification is a challenging task which utilizes the traditional methods due to its more execution time and ineffective decision making. To overcome this problem, this research proposes an automatic tumour classification method named as Hybrid Kernel based Fuzzy C-Means clustering - Convolutional Neural Network (Hybrid KFCM-CNN) method. The performance of Hybrid KFCM- CNN method is validated using T1-Weighted Contrast Enhanced Magnitude Resonance Imaging (T1 -W CEMRI) database. A tumour portion is segmented from the MRI brain image by using Hybrid KFCM method. After the segmentation process, statistical features and super pixels based SIFT, Discrete Cosine Transform (DCT) methods were performed on the segmented image to enhance feature subsets. The best feature values were given to the CNN classifier as an input; it is classified into different types of brain tumours such as Meningioma, Glioma and Pituitary. The experimental outcome shows that the Hybrid KFCMCNN method improved the accuracy of brain tumour classification up to 14.06 % than existing classifiers: SVM and CNN.