Enhanced Brain Tumor Detection and Classification Using Transfer Learning and Explainable AI with Clinical Report Generator
TL;DR
The results obtained here show that transfer learning yields higher accuracy and quicker convergence than building a CNN from the ground up, and together with the built-in explainability and automated report writing, this makes AMC-NeuroDx a practical candidate for real clinical settings.
Abstract
Correctly sorting brain tumors captured through Magnetic Resonance Imaging (MRI) plays a vital role in timely diagnosis and sound clinical decision-making, since a delayed or wrong call can seriously harm patient outcomes. This work introduces AMC-NeuroDx, an upgraded deep-learning pipeline that pits a Custom Convolutional Neural Network (CNN) built from the ground up against a ResNet50 transfer-learning model on a four-way brain tumor classification task (Glioma, Meningioma, Pituitary Tumor, and No Tumor), drawing on 7,223 MRI scans taken from the Kaggle Brain Tumor MRI dataset. ResNet50 follows a two-stage routine—training with a frozen base before full fine-tuning—and reaches 93.16% validation accuracy after only 20 epochs, whereas the Custom CNN needs 50 epochs to hit 90.70%. The framework also embeds Grad-CAM so that spatial heatmaps can show which brain regions drove each prediction, meeting the clinical demand for transparency. On top of this, a Streamlit interface lets users upload MRI scans in real time and automatically produces patient-specific PDF clinical reports holding the diagnosis, confidence scores, Grad-CAM overlays, and recommended next steps. The results obtained here show that transfer learning yields higher accuracy and quicker convergence than building a CNN from the ground up, and together with the built-in explainability and automated report writing, this makes AMC-NeuroDx a practical candidate for real clinical settings.