Brain Tumor Classification System

• Github URL: Project Link

Developed a comprehensive brain tumor classification model using transfer learning techniques with ResNet and VGG-16 architectures to analyze MRI images. The model achieved over 90% accuracy in differentiating tumor types.

Employed robust data preprocessing and augmentation strategies to enhance the quality of training data, which significantly improved model performance.

Fine-tuned the model using TensorFlow to ensure optimal classification accuracy. The solution was integrated with Flask and TensorFlow Serving, enabling real-time and reliable tumor classification for medical applications.