Hybrid RGSA and Support Vector Machine Framework for Three‐Dimensional Magnetic Resonance Brain Tumor Classification

Abstract

A novel hybrid approach for the identification of brain regions using magnetic resonance images accountable for brain tumor is presented in this paper. Classification of medical images is substantial in both clinical and research areas. Magnetic resonance imaging (MRI) modality outperforms towards diagnosing brain abnormalities like brain tumor, multiple sclerosis, hemorrhage, and many more. The primary objective of this work is to propose a three-dimensional (3D) novel brain tumor classification model using MRI images with both micro- and macroscale textures designed to differentiate the MRI of brain under two classes of lesion, benign and malignant. The design approach was initially preprocessed using 3D Gaussian filter. Based on VOI (volume of interest) of the image, features were extracted using 3D volumetric Square Centroid Lines Gray Level Distribution Method (SCLGM) along with 3D run length and cooccurrence matrix. The optimal features are selected using the proposed refined gravitational search algorithm (RGSA). Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002). The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

Characterization of PET/CT images using texture analysis: the past, the present… any future?

“…In a recent study, 31 of 101 patients were used for validation, and the first 70 for building the model [95]. Contrary to the field of PET/CT, the use of machine-learning techniques including robust feature selection, the combination of features within classifiers and cross-validation or validation in an external cohort are well established in the fields of CT [7, 77, 108, 109] and MR [110–114] radiomics.…”

Clickbait Detection for Amharic Language using Deep Learning Techniques

“…Following that, word embeddings of these textual contents are created, and they are then used as input for various neural network models. [35] utilized word2vec to detect Amharic hate speech in social networks and achieved sound results. Word2vec word-level embedding creates a 300-dimensional vector using a continuous bag of words architecture.…”

Dense Hierarchical CNN – A Unified Approach for Brain Tumor Segmentation

“…However, there are certain variations and distinctions between these segments and few images possess weak inter-category features which can easily be classified and few images with strong attributes are difficult to classify [15]. Latest technological advancements in deep learning (DL) have demonstrated accelerated progress, and deep convolutional neural networks (CNNs) have super-seeded in brain tumor classification and segmentation [16]. In particular, CNN has achieved exemplary success in the field of image segmentation with its efficacy in employing a hierarchical classification structure.…”