A Multi-layered Approach to Brain Tumor Classification Using VDC-12

This research paper presents a deep convolutional neural network (CNN) model approach along with feature extraction for multiclass brain tumor detection using medical imaging. Compared to traditional methods that rely on manual interpretation of scans, the VDC-12 (Very Deep Convolution) model proposed in this study has the potential to enhance the accuracy of detecting brain tumors. We used a dataset of MRI brain images containing four categories of tumors, namely meningioma, glioma, pituitary, and normal brain tissue. To evaluate the proposed CNN model’s performance, various metrics were used, including accuracy, precision, recall, and F1 score. According to the experimental results, the VDC-12 model surpasses several cutting-edge techniques, achieving a classification accuracy of 97.60%. This model exhibits promise in the early detection of brain tumors, which can facilitate timely diagnosis and treatment.