Abstract:
Detection and classification of tumor portions from the brain images are the challenging and demanding tasks in the field of medical imaging applications. Because, the earlier prediction of tumor is highly essential for the patients to provide proper treatment at the time. So, an automated tumor detection system can be more useful for the medical experts to identify the growth and structure of tumor region. For this purpose, there are different types of medical image processing techniques are developed in the existing works. The main aim of this work is to present the comprehensive survey for analyzing the techniques used for detecting the brain abnormalities. Also, it objects to investigate the operating characteristics, working nature and performance of various image processing techniques. Typically, the preprocessing techniques are mainly used to filter the noisy contents for improving the quality of images with increased contrast. Specifically, the feature extraction models are used for extracting the high level feature attributes and patterns from the filtered image. Then, the optimal numbers of features are selected with the help of optimization techniques by estimating the objective function based on the best fitness value. Here, the importance of using segmentation approaches is to partition the image into collection of pixels, which are helpful for locating the tumor, affected regions. Finally, the classifiers are used for predicting the output label as normal or tumor-affected by training the samples based on the optimal features. For experimental validation, there are different measures have been used to evaluate the performance results of these techniques.
Page(s):
3672-3685
DOI:
DOI not available
Published:
Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 11, Year: 2022
Keywords:
segmentation
,
Magnetic Resonance Imaging MRI
,
BRATS Dataset
,
Brain Tumor
,
Feature Optimization
,
Deep Learning and Machine Learning Techniques