Autonomous Brain Tumor MRI Classification (95% Accuracy / ResNet50 Backbone) - Deployed via PyTorch by a 14-Year-Old Independent Researcher
Hello Reddit Community,
I am excited to officially share my independent research and live deployment for autonomous neuro-oncology diagnostics. For the past two months, I have been engineering deep learning computer vision pipelines using PyTorch to solve a major clinical challenge: visual boundary overlaps and structural confusion within the confusion matrix between Glioma and Meningioma tissue anomalies.
By implementing Transfer Learning via a ResNet50 Backbone, combined with a custom deep multi-layer feature classifier and cost-sensitive class-weight optimization, the architecture successfully separated identical gray-level tissue features.
📊 Evaluation Metrics (Validated rigorously on 1,600 unseen clinical images):
- Overall Test Accuracy: 95%
- Glioma Precision: 1.00 (Zero false-positive alarms)
- Meningioma Recall: 0.99 (Flawless sensitivity)
- Normal Tissue Protection: 0.99 Recall (Ensuring diagnostic patient safety against false negatives)
I have packaged the finalized weights and successfully deployed a secure, live production environment on Hugging Face for instant expert audit and real-time medical image rendering.
As a 14-year-old independent researcher, my ultimate ambition is to push the boundaries of computational biomedicine and data science. I would be deeply grateful to receive your engineering feedback, architecture critiques, or any suggestions on how to further scale this pipeline!
🌐 Live Production System: https://github.com/IsraBouchentouf-researcher
💻 Open-Source Codebase Audit: https://github.com/IsraBouchentouf-researcher
Thank you for your time and professional guidance!