Brain Tumor Segmentation of MRI Images: A Comprehensive Review on the Application of Artificial Intelligence Tools

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Nandam Gayatri, Sangepu Nagaraju, Raghuram Bhukya, Lasya Priya Reddy Dhodla, Charan Raj Juluru, Jayanth Sai Rajulapati


This pioneering study delves into advanced medical imaging with the goal of accelerating early detection in the complex landscape of brain tumors. Using cutting-edge Artificial Intelligence (AI) tools such as U-Net, Deep Residual Unet Networks (ResUNet), and Inception V3, our research focuses on the complex segmentation of Magnetic Resonance Imaging (MRI) data. ResUNet's deep residual learning architecture addresses vanishing gradient issues, improving the model's ability to detect subtle features within images. U-Net, a convolutional network, excels at image segmentation by combining long paths and capturing contextual information. Inception V3 (Inception Version 3) is distinguished by its inception modules, which strategically process multi-scale features while optimizing the model for intricate pattern recognition. Using these cutting-edge algorithms, our methodology accurately identifies and delineates brain tumors from MRI scans. The combination of ResUNet's depth, U-Net's segmentation abilities, and Inception V3's multi-scale analysis results in a strong and efficient system for early tumor detection. Our findings have far-reaching implications, opening the door to transformative clinical applications.

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