TumorClassifier

Inspiration I wanted to build a brain tumor detection system because early detection can make a big difference in medical decisions and patient care. I was inspired by the idea of using AI to support doctors and make diagnosis faster, smarter, and more accessible.

What it does I created a deep learning model that analyzes MRI images and predicts whether they show signs of a tumor or not. I trained the system to study brain scan patterns and give a result based on what it learns from the dataset.

How I built it I built this project by collecting MRI image data, organizing it into tumor and non-tumor classes, and training a ResNet-based neural network. I used PyTorch for model training, preprocessing, and evaluation, and I tested the model to check how accurate its predictions were.

Challenges I ran into I faced several challenges while building this, including handling image data properly, balancing the dataset, and improving the model’s accuracy. I also had to tune the training process carefully to avoid overfitting and make sure the model learned the right patterns.

Accomplishments that I’m proud of I’m proud that I built a working tumor classification model and trained it to produce predictions from MRI images. I’m also proud that I was able to improve the model through experimentation and get meaningful results from the dataset.

What I learned I learned how important data quality, preprocessing, and model tuning are in machine learning projects. I also learned how deep learning can be used in healthcare and how much careful testing and adjustment goes into building a reliable model.

What’s next for TumorClassifier I want to improve the model further by using a larger dataset, testing more advanced architectures, and making the prediction system easier to use in a real-world application. I also want to add features like better accuracy checks, user-friendly output, and a more practical interface for medical use.