Brain Tumor Detection and Classification by MRI Using Biologically Inspired Orthogonal Wavelet Transform and Deep Learning Techniques.

Muhammad Arif,Shermin Shamsudheen,Oana Geman,Diana Izdrui,Dragos Vicoveanu,F Ajesh,Liaqat Ali

doi:10.1155/2022/2693621

Abstract

Radiology is a broad subject that needs more knowledge and understanding of medical science to identify tumors accurately. The need for a tumor detection program, thus, overcomes the lack of qualified radiologists. Using magnetic resonance imaging, biomedical image processing makes it easier to detect and locate brain tumors. In this study, a segmentation and detection method for brain tumors was developed using images from the MRI sequence as an input image to identify the tumor area. This process is difficult due to the wide variety of tumor tissues in the presence of different patients, and, in most cases, the similarity within normal tissues makes the task difficult. The main goal is to classify the brain in the presence of a brain tumor or a healthy brain. The proposed system has been researched based on Berkeley's wavelet transformation (BWT) and deep learning classifier to improve performance and simplify the process of medical image segmentation. Significant features are extracted from each segmented tissue using the gray-level-co-occurrence matrix (GLCM) method, followed by a feature optimization using a genetic algorithm. The innovative final result of the approach implemented was assessed based on accuracy, sensitivity, specificity, coefficient of dice, Jaccard's coefficient, spatial overlap, AVME, and FoM.

Highlights

Every year, more than 190,000 people in the world are diagnosed with primary or metastatic brain tumors
Researchers in [7] proposed a strategy to recognize MR brain tumors using a hybrid approach incorporating Discrete wavelet transformation (DWT) transform for feature extraction, a genetic algorithm to reduce the number of features, and to support the classification of brain tumors by vector machine (SVM) [8]. e results of this study show that the hybrid strategy offers better output in a similar sense and that the RMS error is state-of-the-art
E features are extracted by using the gray-level-co-occurrence matrix (GLCM) algorithm. e genetic algorithm is used for selecting the features

Summary

Introduction

More than 190,000 people in the world are diagnosed with primary or metastatic brain (secondary) tumors. Researchers in [7] proposed a strategy to recognize MR brain tumors using a hybrid approach incorporating DWT transform for feature extraction, a genetic algorithm to reduce the number of features, and to support the classification of brain tumors by vector machine (SVM) [8]. E region should be segmented by Berkeley’s wavelet transformation and extract the texture features using ABCD, FOS, and GLCM features Classifiers such as Naıve Bayes, SVM-based BoVW, and CNN algorithm should compare the classified result and must identify the tumor region with high precision and accuracy. E rest of the article is intended to continue: section 1 presents the background to brain tumors and related work; section 2 presents the construction techniques with the measures used throughout the method used; section 3 describes the results and analysis and the comparative study; and, section 4 presents the conclusions and upcoming work

Methodology

Features Selection

Findings

Conclusion