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Numerical Simulation and Design of Efficient Brain Tumor Segmentation Using Machine Learning Techniques: A Comprehensive Review

Shivani Gupta, Sultan Singh Saini

Abstract


Experts have created tools to help in tumour classification and picture processing. It is common practise to segment the clinical picture in order to identify tumours. The majority of researchers and analysts are attempting to construct this tool and enhance it with more features. This technique uses a Matlab GUI to separate brain tumours from MRI pictures. The GUI will be used to identify which division patterns, channels, and other image processing techniques produce the best results. Using GUI-based matlab programmes for successful segmentation of brain tumours is the most critical part of this study. This allows us to employ a variety of picture processing methods and different channel combinations to display the best results that will help us distinguish early stages of brain tumours. Researchers’ input has been taken into account in the development of biomedical image management, and a few issues with picture preparation in this field have been identified. Biomedical images (MRI images) have a serious problem with the picture division approach because it is frequently implemented. Prerequisites for divisive CT scanning, for example, include MRI image division. When it comes to photography, there is no such thing as a one-size-fits-all approach. They cannot be the same as any other MRI scans, because a divisional system with the same focus would yield a different conclusion. Despite this, there is no strategy that is more universally applicable and capable of being applied to a wider range of clinical information for each individual clinical picture. This gives satisfactory outcomes. Regardless, differentiating strategies based on a specific imaging application can often yield superior outcomes if prior information is taken into account. The review article focuses on a complete examination of the various tools and techniques involved in the study and design of machine learning-based classification and detection of brain tumours for biomedical and telemedicine purposes.


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References


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