Efficient Methods for Improving Brain Tumors Diagnosis in MRI Scans Using Deep Learning

Accurate diagnosis of brain tumors, such as glioma, meningioma, and pituitary tumors, from MRI scans is critical for effective treatment planning. This paper presents an innovative approach for brain tumor classification using hybrid models based on feature-level fusion of multiple Convolutional Neural Network (CNN) architectures, including ResNet, EfficientNet, AlexNet, VGG-Net and DenseNet. Additionally, an original CNN model was developed having a straightforward, yet highly efficient architecture. The proposed models were trained and tested on a well-balanced dataset of 5,721 MRI samples. The experimental results demonstrate that fusion-based hybrid models, integrating ResNet50 with other architectures, achieve high classification accuracy, outperforming single-model approaches. Furthermore, we implemented two majority voting schemes, which further enhance prediction reliability. This work contributes to the advancement of automated brain tumor diagnosis, offering promising solutions with potential clinical applications.