Enhancing Brain Tumor Detection Using Convolutional Neural Networks in Medical

It is commonly known that early and accurate brain tumour detection is critical, which emphasizes the need for better diagnostic techniques that improve patient outcomes. Although they are the mainstay of diagnosis, conventional imaging methods like CT and MRI scans are frequently limited by their requirement for extreme precision. In order to improve brain tumour detection in medical imaging, this study explores the effectiveness of convolutional neural networks, or CNNs, with a particular emphasis on the U-Net and DenseNet architectures. Through the use of a complex methodology that involves extensive validation procedures, rigorous model training, and data augmentation, the study shows a significant improvement in the accuracy of brain tumour detection. The results confirm that CNNs outperform conventional techniques in more accurately determining and categorizing brain tumour kinds and margins. This development clarifies the significant influence of deep learning in neuro-oncology, pointing to a crucial shift toward AI-enhanced diagnostic methods in clinical settings and establishing the framework for further study in more extensive and diverse datasets