Segmentation and Analysis of Brain Tumor using Meta-Heuristic Algorithm

Today, all scientific advancements related to medical image processing aim to develop a unique computational model. The latter will mimic the way humans interpret images. In the present paper, we propose a formal approach biologically inspired by the human natural vision system’s mechanisms. To that end, we use the spiking neural network model for edge detection in brain MRI images. The optimal results provided by this last step increase performance, and facilitate the process of anomaly detection. For that, we have developed a tool called Edge and Anomaly Detection of Brain MRI Images in Distributed Environment (EADBMIDE). It is tested on an MRI brain tumour dataset, which shows the effectiveness of the proposed methods. Each stage in this tool is compared separately with similar approaches in the literature. The obtained results show a significant improvement, making this a recommended tool for edge and anomaly detection methods in MRI images.

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Edge and anomaly detection of brain magnetic resonance images in a distributed environment

Chaoui

Ghomari

Meftah

2021

Int J Imaging Syst Tech

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A Comprehensive Survey on Brain Tumor Diagnosis Using Deep Learning and Emerging Hybrid Techniques with Multi-modal MR Image

Ali

Pei

et al. 2022

Arch Computat Methods Eng

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A Review on State-of-the-Art Techniques for Image Segmentation and Classification for Brain MR Images

Aswathy

Abraham

2023

CMIR

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The diagnosis of tumors in the initial stage plays a crucial role in improving the clinical outcomes of a patient. Evaluation of brain tumors from many MRI images generated regularly in a clinical environment is a complex and time-consuming process. Therefore,there comes a need for an efficient and accurate model for the early detection of tumors. This paper revolves around the current strategies used for brain tumor segmentation and classification from MRI images of the brain. This approach also tries to pave the way for the significance of their performance measure and quantitative evaluation of forefront strategies. This state of the art clearly describes the importance of several brain image segmentation and classification methodsduring the past 13 years of publication by various researchers. In this instance, new calculations are being made for potential clients to analyze the concerned area of research. This review acknowledges the key accomplishments expressed in the diagnostic measures and their success indicators of qualitative and quantitative measurement. This research study also explores the key outcomes and reasons for finding the lessons learned.

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Segmentation and Analysis of Brain Tumor using Meta-Heuristic Algorithm

Cited by 3 publications

References 20 publications

Edge and anomaly detection of brain magnetic resonance images in a distributed environment

Edge and anomaly detection of brain magnetic resonance images in a distributed environment

A Comprehensive Survey on Brain Tumor Diagnosis Using Deep Learning and Emerging Hybrid Techniques with Multi-modal MR Image

A Review on State-of-the-Art Techniques for Image Segmentation and Classification for Brain MR Images

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