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Original Article | Computer Science | Volume 15 Issue 4, April 2026 | Pages: 693 - 696 | India
Enhanced Brain Tumor MRI Analysis Using Advanced Noise Reduction and Deep Learning
Abstract: Brain tumor detection using Magnetic Resonance Imaging (MRI) is a critical task in medical image analysis, as early and accurate diagnosis significantly improves patient survival and treatment planning. However, MRI images often suffer from noise, intensity inhomogeneity, and low contrast, which adversely affect automated detection and segmentation performance. This paper presents an intelligent preprocessing framework integrated with deep learning techniques to enhance MRI image quality and improve brain tumor detection accuracy. The proposed approach employs advanced noise reduction, skull stripping, and intensity normalization techniques prior to deep feature extraction using convolutional neural networks. These preprocessing strategies significantly improve image quality, enabling more accurate and robust tumor detection. Experimental results demonstrate that the proposed framework significantly improves classification and segmentation performance compared to conventional methods.
Keywords: Brain Tumor Detection, MRI Image Processing, Noise Reduction, Deep Learning, U-Net Architecture
How to Cite?: Dr. Rajshree, "Enhanced Brain Tumor MRI Analysis Using Advanced Noise Reduction and Deep Learning", Volume 15 Issue 4, April 2026, International Journal of Science and Research (IJSR), Pages: 693-696, https://www.ijsr.net/getabstract.php?paperid=SR26408151832, DOI: https://dx.dx.doi.org/10.21275/SR26408151832