MINDMAPPER: Investigating Brain Tumors Through CNN Techniques

Brain tumors pose significant health challenges, necessitating early diagnosis and effective treatment. Traditional diagnosis via MRI scans is time-consuming and prone to error. We propose a deep learning approach using convolutional neural networks (CNNs) to analyze brain tumors from MRI scans, aiming to enhance diagnostic accuracy and efficiency. Our method includes image normalization and enhancement, followed by CNN-based feature extraction and classification into tumor-present or tumor-absent categories. Training involves optimizing the loss function using gradient descent. This system achieved an accuracy of 98%, demonstrating its potential to revolutionize brain tumor diagnostics and improve patient care. Further research and clinical trials are needed for practical implementation.