Hi people! 👋 Welcome to the realm of cutting-edge medical innovation! 🧠

Imagine a world where the detection, classification, and segmentation of brain tumors are seamlessly integrated into a powerful Python-based system. Behold the synergy of Python's prowess, YOLOv8's precision, and Streamlit's user-friendly interface converging to create an extraordinary brain tumor detection, classification, and segmentation project.

Try out TumorVision here: https://brain-tumor-fyp.streamlit.app/

💡 Key Approaches:

Model Training 👉 A pre-trained YOLOv8 model by Ultralytics is utilized for Classification & Segmentation tasks in transfer learning.

Frontend Deployment 👉 The Streamlit framework integrates the models with the frontend interface after model training.

Optimization 👉 Hyperparameter adjustment, such as optimizers and epochs, refines the model, resulting in optimization.

Results 👉 The model showcases remarkable performance, achieving approximately 89% accuracy in segmentation and 96% accuracy in classification post-finetuning, underscoring its reliability and precision