Automated Brain Tumor Detection Using Convolutional Neural Network

This study investigates the efficacy of advanced deep learning techniques, specifically convolutional neural network (CNN) (U-Net) and single-shot multibox detector (SSD), in enhancing the early detection of brain tumors, thereby facilitating timely medical intervention. Accurate brain tumor detection is paramount in medical image analysis as it involves the precise identification and localization of abnormal growths within the brain. Conventional diagnostic approaches often rely on manual analysis conducted by radiologists, which are susceptible to human error and influenced by variability in tumor size, shape, and location. In our research, we leverage U-Net, a CNN widely recognized for its effectiveness in medical image segmentation, alongside SSD, an established object detection algorithm. The results indicate that the U-Net model achieved an impressive accuracy of 97.73%, demonstrating a high level of effectiveness in segmenting brain tumors with exceptional precision. Conversely, the SSD model secured an accuracy of 58%, which, while comparatively lower, suggests that it may still serve as a valuable supplementary tool in specific scenarios and for broader applications in identifying tumor regions within medical scans. Our findings illuminate the potential of utilizing U-Net for high-precision brain tumor detection, reinforcing its position as a leading method in medical imaging. Overall, the study reinforces the important role of deep learning methods in improving early detection outcomes in neuro-oncology and highlights avenues for further exploration in enhancing diagnostic accuracy.