Classifying Brain Tumor in MRIs Using Spatial Attention-Based Adaptive Inception Residual Net and Localizing Abnormalities via Hyperdense Inception 3D Trans-UNet + + Model

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Novel deep learning model improves segmentation and classification of brain tumors in MRI images, suggesting enhanced diagnostic potential.

The proposed model achieves an accuracy of 95.50% in classifying brain tumors, surpassing traditional methods.
Hyperdense Inception 3D Trans-UNet is utilized for effective segmentation of tumor tissues from MRI images.
Spatial Attention-based Adaptive Inception Residual Net is employed for enhanced classification accuracy.
Implemented algorithms reduce overfitting and improve the model's performance during training.

Pushparaj. et al. (2025) studied this question.

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Novel deep learning model improves segmentation and classification of brain tumors in MRI images, suggesting enhanced diagnostic potential.