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Conference

MRI Brain Tumor Detection using Deep Learning and Feature Fusion Techniques

Balas Subalatha.M
Jul 2026 · 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT) · pp. 188-193 · 0 citations · 18 references

Abstract

Magnetic resonance imaging (MRI) is a crucial component of the medical diagnostic and therapeutic approach for brain tumors, as early detection of anomalous tissue considerably enhances patient outcomes. MRI images may obscure significant tumor characteristics owing to noise, inadequate contrast, and erratic intensity distributions. This work examines the application of ResNet169, EfficientNetB0, and a hybrid fused model (ResNetEffi169B0) to address challenges in deep learning-based image improvement and categorization. The performance metrics included F1-score, Accuracy, Precision, Sensitivity, Entropy, SSIM, and MSE to test the models. The results show that EfficientNetB0 has the best PSNR (11.45), SSIM (0.308), and accuracy (0.601) when it comes to classification and improvement. The ResNet169 model's high sensitivity of 0.463 showed that it could dependably find tumor regions. The hybrid approach used the best features of both styles to create balanced results that made diagnoses more consistent and images clearer. The fusion method enhances the quality and structural data of magnetic resonance imaging (MRI) scan slices, leading to more precise classification and enhanced tumor visibility. This study examines the potential of hybrid deep learning models to enhance computer-aided diagnostic tools in medical imaging for improved brain tumor identification.

DOI Semantic Scholar
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