Overview This project aims to classify brain tumor images using a Convolutional Neural Network (CNN). The model is designed to analyze medical images of brain scans and accurately categorize them based on the presence or absence of tumors.
Key Features CNN Architecture: The project employs a carefully crafted CNN architecture, tailored for the unique challenges of medical image classification. Dataset: Utilize a curated dataset containing labeled brain tumor images for both training and evaluation. Model Training: Train the CNN model on the labeled dataset to recognize patterns associated with tumor presence. Evaluation: Evaluate the model's performance using key metrics such as accuracy, precision, recall, and F1-score. Normalization: Implement preprocessing techniques, including image normalization, to enhance the model's generalization capabilities.
Technologies Used TensorFlow: Deep learning framework for building and training the CNN model. Python Libraries: NumPy: Numerical computing library. Pandas: Data manipulation and analysis. Matplotlib: Data visualization. Scikit-learn: Machine learning tools, including train-test split. OpenCV: Image processing library for handling and transforming medical images.
Author: Abhishek Jolad