A Deep Learning Framework with Intelligent Preprocessing for Robust Brain Tumor MRI Analysis

Author(s)	Dr. Rajshree
Country	India
Abstract	Accurate analysis of brain tumors from Magnetic Resonance Imaging (MRI) is a critical task in medical image computing, as it directly influences diagnosis, treatment planning, and patient survival. Although MRI provides excellent soft tissue contrast, acquired images often suffer from noise, low contrast, and intensity inhomogeneity, which adversely affect automated tumor detection and segmentation systems. This paper presents a robust deep learning framework integrated with intelligent preprocessing techniques for effective brain tumor MRI analysis. The preprocessing stage incorporates advanced denoising, skull stripping, intensity normalization, and contrast enhancement to improve image quality prior to deep learning inference. A U-Net–based convolutional neural network is employed for accurate tumor segmentation. Experimental evaluation on benchmark datasets demonstrates that the proposed framework significantly improves segmentation accuracy from 91.4% to 98.1%, Dice score from 0.88 to 0.96, and F1-score from 0.89 to 0.97 compared to conventional deep learning approaches without preprocessing.
Keywords	Brain Tumor MRI, Intelligent Preprocessing, Deep Learning, U-Net, Image Segmentation
Field	Computer
Published In	Volume 17, Issue 1, January-June 2026
Published On	2026-03-07
Cite This	A Deep Learning Framework with Intelligent Preprocessing for Robust Brain Tumor MRI Analysis - Dr. Rajshree - IJAIDR Volume 17, Issue 1, January-June 2026.

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Journal of Advances in Developmental Research