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

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.

Intelligent Wearable Devices Enabled Automatic Vehicle Detection and Tracking System with Video-Enabled UAV Networks Using Deep Convolutional Neural Network and IoT Surveillance

“…Lee et al [ 18 ] present a modified method to the mean shift algorithm for head tracking, which they call the modified technique. Whenever an item crosses beyond the border, the tracking algorithm is unable to locate the intended target [ 19 ]. The problem of convergence inside the border is resolved using a modified approach.…”

Smart Healthcare System with Light-Weighted Blockchain System and Deep Learning Techniques

“…The amount of residual energy (measured in battery volume) obtainable in the system per unit of time may be tracked using this account, and the account is updated in real-time when the network's residual energy changes [ 22 ]. This is the total quantity of energy created by all nodes, regardless of the kind of node that is being utilized to generate the energy.…”

Assessment of Dynamic Swarm Heterogeneous Clustering in Cognitive Radio Sensor Networks

“…The ICCA that has been proposed ranks the CHs according to the fitness function at the lowest possible cost. It will find the CRSN node with the highest cost fitness function among those available if there are a big number of CRSN nodes available, and it will converge more quickly if there are a large number of CRSN nodes available [28]. The lifetime of CRSN energy is prolonged in the case of efficient clusters that are employed to construct the dynamic channel allocation system, as shown in Figure 1.…”

“…Spectrum management is responsible for determining the physical attributes of the spectrum, the reconfiguration operations of the CR, and the selection of users' needs for the spectrum in question. To improve the overall criteria in terms of overall quality of service spectrum, the most appropriate spectral band from the next-generation network is selected and used to increase the overall criteria [28]. This is done to increase the overall criteria in terms of overall quality of service spectrum.…”