Tumor Classification

This project aimed to classify tumors using machine learning algorithms based on medical imaging data.

Key Features:

• Utilized deep learning models for image recognition and classification.
• Trained on a large dataset of medical images to distinguish between benign and malignant tumors.
• Implemented a user-friendly interface for uploading images and obtaining classification results.

Technologies Used:

• Python
• PyTorch
• Scikit-learn
• Matplotlib
• Medical Imaging Libraries (e.g.
• DICOM
• OpenCV)

Challenges and Solutions:

One major challenge was handling the complexities of medical imaging data and ensuring robust model performance. This was addressed by preprocessing techniques and fine-tuning the neural network architectures.

Outcome:

The project achieved an accuracy rate of 90%+ in classifying tumors, demonstrating its potential for assisting medical professionals in diagnosis.

Code Repository:

Find the code on GitHub

Demo:

Stay tuned for a live demo!