InsightBrain: CNN-Based Binary Classification of Brain Tumor Images

When a brain tumor is in its most advanced stages, it is considered a highly malignant medical condition with an adverse outcome. To improve patient survival rates and ensure proper medical attention, accurate diagnosis is essential. Computer-aided tumor detection systems, especially convolutional neural networks (CNNs), have shown encouraging outcomes in this field by utilizing deep learning developments. CNNs outperform traditional neural network architectures by extracting robust features automatically from input data. This paper presents a methodology for the binary classification of cerebral tumors using CNN architecture. Furthermore, MRI scans are subjected to data augmentation procedures to improve generalization, expand the size of the dataset, and reduce the likelihood of overfitting. The results demonstrate that the suggested CNN architecture produces the best classification accuracy, up to 97.29% along with an overall precision of 98% and recall of 97.85%. which helps in proving the effectiveness of CNN-based methods for recognizing and categorization of brain tumors in MRI scans. The medical field may find great use for this suggested architecture in helping to detect brain tumors early on. By streamlining the health evaluation procedure for patients, this technology might be put into use, which could lower the death rates linked to brain tumors.