Classification of MRI Brain Tumor Images using Deep Learning Segment Anything Model for segmentation and Deep Convolution Neural Network
Head of Data Science and AI, AW Rostamani, UAE.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 1153–1161
Publication history:
Received on 05 July 2024; revised on 12 August 2024; accepted on 14 August 2024
Abstract:
Brain tumors pose a significant health challenge by putting pressure on healthy parts of the brain or spreading into other areas and blocking the flow of fluid around the brain. Thus, identifying and categorizing the tumor is crucial for delivering effective treatment, especially if detected early. This means the tumor is smaller, and treatment is more effective, less invasive, and has fewer side effects.
In recent years, many researchers have developed computer vision, and more specifically, deep learning methods, to automate the analysis of brain MRI scans. These methods enable efficient processing and improve the accuracy of detecting small tumors.
This paper aims to propose a deep-learning method for classifying brain tumors. In this work, the input image goes through two subprocesses: first, object detection to identify the tumor's location. Then, a fine-tuned Segment Anything Model (SAM) was applied to extract the lesion from the background. Finally, deep learning Convolution Neural Network (CNN), is applied to the cropped image for classification. This method will help doctors and researchers detect tumors at the initial stages
Keywords:
Brain tumor; Image processing; Feature extraction; Machine learning; MRI image Classification; Computer vision; Cancer classification; Convolution Neural Network; Cross-modal deep learning
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0