Identification of Brain Tumour Detection And Localization Using Computational Techniques

Magnetic resonance imaging is a widely used technique for good medical treatment. Magnetic resonance imaging also known as MRI, is the most advanced technology that provides a lot of information about the anatomy of human tissues. Transfer learning, which is a subset of machine learning, has been de...

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Bibliographic Details
Published in:	2023 4th IEEE Global Conference for Advancement in Technology (GCAT) pp. 1 - 4
Main Authors:	Ghosh, Akansha, Shrotriya, Anita, Shekhawat, Deepika, Mahajan, Ginika
Format:	Conference Proceeding
Language:	English
Published:	IEEE 06-10-2023
Subjects:	Brain modeling Brain Tumor Feature extraction Image analysis Image segmentation Location awareness Magnetic resonance imaging Transfer learning
Online Access:	Get full text
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Summary:	Magnetic resonance imaging is a widely used technique for good medical treatment. Magnetic resonance imaging also known as MRI, is the most advanced technology that provides a lot of information about the anatomy of human tissues. Transfer learning, which is a subset of machine learning, has been demonstrated to enhance the effectiveness of machine learning models in various applications, including medical image analysis. It involves using a pre-trained model to extract features and train classifiers on the extracted features. Transfer learning [14] has also been used for image analysis tasks. Image segmentation is used for the extraction of multiple features from an image that can be combined or segmented to create objects of interest that can be identified and interpreted. This article is concerned with the detection of brain tumors from MRI scans using transformational learning.
DOI:	10.1109/GCAT59970.2023.10353497