Transfer Learning for Brain Tumor Detection

This project focuses on brain tumor detection using multiple transfer learning models, such as VGG16, VGG19, DenseNet121, EfficientNetB7, and others. It explores the impact of using pre-trained weights (ImageNet) versus training from scratch (None) to compare model performance. Additionally, the role of image augmentation is tested to evaluate its effects on improving the generalization and accuracy of these models. By combining advanced deep learning architectures and data augmentation techniques, the aim is to enhance the accuracy and reliability of brain tumor classification, providing a robust AI-based solution to aid in early detection and diagnosis in healthcare.