MRI-BASED BRAIN TUMOR LEVEL DETECTION USING DEEP LEARNING TECHNIQUES

Author(s)	Sonia, Dr. Asha . Rani
Country	India
Abstract	This study presents an automated framework for brain tumor level detection using deep learning techniques applied to MRI images. The Kaggle Brain Tumor dataset, comprising 3,000 images, was utilized to train and evaluate two state-of-the-art convolutional neural network architectures: VGG19 and ResNet50. The methodology involved preprocessing MRI scans, implementing transfer learning, and fine-tuning hyperparameters to optimize classification performance. Accuracy was employed as the primary evaluation metric, with ResNet50 demonstrating superior performance compared to VGG19 due to its deeper residual connections and reduced vanishing gradient issues. The results confirm that deep learning techniques can effectively classify tumor levels, thereby support clinical decision-making and reduce diagnostic delays.
Keywords	Brain tumour Detection, Machine learning, Deep Learning, Accuracy.
Field	Computer Applications
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-19
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.66886

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International Journal For Multidisciplinary Research