Transfer Learning: Brain Tumor Classification with VGG16 Architecture

I recently completed a fascinating deep learning project aimed at classifying brain tumors using MRI images. The dataset I worked with was binary-labeled — "yes" for the presence of a tumor and "no" for no tumor. My goal was to develop an accurate model that could help in detecting brain tumors using this data.

The first step was data preprocessing. I resized all MRI images to a standard size of 224x224 pixels and organized them into arrays. One array held the images, and another contained the corresponding labels. I then split the dataset into 77% for training and 33% for testing, ensuring a balanced approach to training and validation. To handle the categorical labels, I used a label encoder, transforming them into numerical values for the model to process effectively.

For the model architecture, I opted for the VGG16 pretrained model, which has shown great success in image classification tasks. I fine-tuned the model by freezing the last 4 layers, preserving the base network’s learned features, while I added custom layers on top to adapt it to the brain tumor classification task. After setting up the architecture, I trained the model for 6 epochs, achieving a 95% training accuracy and an impressive 97% validation accuracy.

#DeepLearning #VGG16 #BrainTumorClassification #MedicalImaging #MachineLearning #AI #HealthcareAI #TransferLearning?#TensorFlow