3D MRI reconstruction and brain tumor diagnosis using deep learning with explainable AI

Timely and accurate identification of brain tumors is crucial for optimizing patient outcomes, guiding surgical planning, and determining appropriate treatment strategies. Despite the advancements in MRI-based tumor detection and artificial intelligence, developing reliable and clinically applicable models remains challenging, particularly in contexts where robustness, interpretability, and consistency are critical. Existing approaches often lack advanced 3D imaging capabilities and robust Explainable AI (XAI) techniques, which limit their diagnostic utility and clinical adoption. To address these limitations, this study proposes a robust deep learning architecture that incorporates the improved EfficientNet B7, Xception, and ResNet152 architectures. We further proposed a novel classification model incorporating enhanced data augmentation and histogram equalization to automatically classify brain MRI images into four diagnostic categories. Three distinct datasets, one segmented dataset, and a merged dataset combining all sources were used to train and evaluate the models. The proposed models achieved accuracies in the range of 97–99%, demonstrating consistent and strong performance across datasets. To enhance diagnostic transparency, XAI techniques, including Grad-CAM and LIME, were employed to provide visual insights into the system’s decision-making processes. Additionally, this study incorporated a novel 3D reconstruction approach across the sagittal, coronal, and axial planes, providing a comprehensive view that supports improved diagnostic accuracy and precise clinical interpretation. Overall, this study demonstrates a novel unified framework integrating classification, segmentation, XAI, current gaps in brain tumor diagnostics and holding significant potential to enhance clinical reliability, transparency, and precision, ultimately improvinge clinical reliability, interpretability, and precision, ultimately supporting better patient outcomes.