Brain-Tumor-Classification-Based-on-deep-learning

I recently worked on a project that involved classifying and detecting brain tumors using deep learning techniques. The project was implemented using Python and several libraries, including PyTorch, YOLO, and scikit-learn.

The goal of the project was to build a system that could accurately classify brain tumors as benign or malignant, as well as detect the location of the tumors within an MRI scan. To achieve this, I used PyTorch to train a deep convolutional neural network (CNN) on a large dataset of MRI scans annotated with tumor labels and bounding boxes.

I also used the YOLO (You Only Look Once) object detection algorithm to enhance the accuracy of the tumor detection component of the system. YOLO is a fast and efficient algorithm that is well-suited for real-time object detection tasks.

Finally, I used scikit-learn to evaluate the performance of the system using various metrics, such as precision, recall, and F1 score.

Overall, the project was a success and the system was able to achieve high levels of accuracy in both tumor classification and detection. The use of deep learning and advanced techniques like PyTorch and YOLO allowed me to build a powerful and effective solution to this important medical problem.