Brain Tumor Classifier with Convolutional Neural Networks
An end-to-end machine learning project to classify brain tumor types from MRI scans. I built and trained a Convolutional Neural Network (CNN) using Python and TensorFlow, achieving 91% accuracy.
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problem
Accurately identifying the type of brain tumor from medical imaging is a critical but time-consuming task for radiologists. I wanted to explore how deep learning could be applied to automate and assist in this diagnostic process.
solution
We implemented a complete computer vision pipeline to classify tumors in MRI scans into three categories. The solution used a ResNet-50 convolutional neural network, which was able to classify tumors with 91% accuracy.
Applying Deep Learning to Medical Imaging
My fascination with the real-world applications of AI led me to medical imaging. I sourced a public dataset of brain MRI scans and set out to build a classifier. One of the biggest challenges was the limited size of the dataset, a common problem in the medical field. I overcame this by implementing data augmentation techniques—like rotating and zooming images—which taught the model to be robust to variations in image orientation and scale. Building and fine-tuning the ResNet-50 CNN architecture was a fantastic learning experience in applied deep learning. This project was a deep dive into the practical realities of building and validating a machine learning model, from handling raw data to interpreting the final performance metrics and achieving 91% classification accuracy.
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