Performance Analysis of Texture Image Retrieval for Curvelet, Contourlet Transform and Local Ternary Pattern Using Mri Brain Tumor Image

Abstract

Texture represents spatial or statistical repetition in pixel intensity and orientation. Brain tumor is an abnormal cell or tissue forms within a brain. In this paper, a model based on texture feature is useful to detect the MRI brain tumor images. There are two parts, namely; feature extraction process and classification. First, the texture features are extracted using techniques like Curvelet transform, Contourlet transform and Local ternary pattern (LTP). Second, the supervised learning algorithm like Deep neural network (DNN) is used to classify the brain tumor images. The Experiment is performed on a collection of 1000 brain tumor images with different orientations. Experimental results reveal that contourlet transform technique provides better than curvelet transform and Local ternary pattern.

Transfer‐learning‐based classification of pathological brain magnetic resonance images

“…Table 6 shows that the proposed transfer learning method based on the pretrained EfficientNet‐B5 outperforms other deep models using a DNN [41] or kernel‐based ELM [28] in terms of the best‐case accuracy for fivefold cross‐validation on real MR images. In addition, our method outperforms other models used in transfer learning, such as GoogLeNet [42], VGG19 [44], and ResNet with a randomized neural network [31].…”