Citation :

Priyanka Gupta, Ramandeep Sandhu, "End-to-End Brain MRI Tumor Analysis: Preprocessing, Segmentation and Classification," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 2, pp. 172-183, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I2P111

Abstract

The goal of this work is to provide a simple and reproducible method that performs three tasks for brain MRI in one flow: consistent preprocessing, accurate tumor segmentation, and reliable multi-class classification. The procedure uses an open dataset organized for both tasks. Preprocessing converts every slice to a common size, applies luminance equalization to improve local contrast, binarizes masks with a fixed threshold, and normalizes images so the model receives stable inputs. A shared residual encoder feeds two light heads: a U-Net style decoder for pixel masks and a small classifier for four labels (glioma, meningioma, pituitary, no tumor). Training alternates segmentation and classification mini-batches with balanced losses, and model selection uses a composite of validation Dice and validation accuracy. The protocol reports all outputs needed for audit and reuse: learning curves, confusion matrices, segmentation overlays, and a history file with metrics. In a ten-epoch run, the method achieved Dice 0.800 and IoU about 0.667 for segmentation, and accuracy 0.929 with macro-F1 0.920 for classification on the test split. The approach is compact, easy to train on a standard workstation, and avoids heavy architecture. It is suitable as a dependable baseline for studies that need both a tumor mask and a case label, and it can be extended to new sites or tasks by adjusting the preprocessing or the loss balance without changing the core design.

Keywords

Brain MRI, Brain tumors, Classification, Deep learning, Segmentation.

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