A Hybrid Convolutional Neural Network and Deep Belief Network for Brain Tumor Detection in MR Images

A Hybrid Convolutional Neural Network and Deep Belief Network for Brain Tumor Detection in MR Images

Early tumor detection in brain plays vital role in early tumor detection and radiotherapy. MR images are used as the input image for brain tumor finding and classify the type of brain tumor. For early detection or prediction of the brain tumor, an improved feature extraction technique along with Deep Neural Network (DNN) has been recommended. First, MR image is pre-processed, segmented and classified utilizing image processing techniques. Support Vector Machine (SVM) based brain tumor classifications are achieved previously with less precision rate. By integrating DCNN(Deep Convolutional Neural Network) classifier and DBN(Deep Belief Network), an improvement in precision rate can be achieved. This paper mainly focuses on six features viz., entropy, mean, correlation, contrast, energy and homogeneity. The proposed method is used to identify the place, locality and dimension (size) of the tumor in the cerebrum through MR copy using MATLAB software. The performance metrics recall, precision, sensitivity, accuracy and specificity are achieved.
8 27 2019 979 985 B11930782S419 10.35940/ijrte.B1193.0782S419 https://www.ijrte.org/wp-content/uploads/papers/v8i2S4/B11930782S419.pdf https://www.ijrte.org/wp-content/uploads/papers/v8i2S4/B11930782S419.pdf