FusionNetX: A Deep Feature Fusion Model Leveraging MRI and Deep Learning for Enhanced Brain Tumor Detection

Brain tumors are considered one of the deadliest diseases in the world, and misdiagnosing them puts patients' lives in danger and lowers the survival rate. Magnetic resonance imaging (MRI) with deep learning-based techniques, especially convolutional neural networks, is crucial for overcoming this obstacle, as it enables a more detailed examination of the brain's internal structure and offers outstanding learning and predictive capabilities. The need for accurate diagnosis of the tumor in the brain is still significant. Therefore, an enhanced feature-fusion-based DL model (FusionNetX) is proposed that leverages the strengths of two pretrained models, DenseNet121 and EfficientNetB7, using customized fine-tuning hyperparameters, including trainable and non-trainable layers. The proposed FusionNetX model was applied to five publicly available brain tumor datasets: Br35H, Figshare, Sartaj, Masoud, and Balanced Brain Tumor. Some preprocessing steps were applied to improve image quality and address overfitting through resizing and data augmentation. Using a variety of performance evaluation metrics, such as accuracy, loss, positive predicted value, negative predictive value, true positive rate, true negative rate, F1-score, false negative rate, and false positive rate, the proposed model FusionNetX was found to have an accuracy of 99.33% for Br35H, 99.13% for Figshare, 99.89% for Masoud, 99.19% for BBT-Dataset, and 98.77% for Sartaj. Additionally, the five brain tumor datasets were evaluated using late fusion and attention-based fusion to demonstrate the significance of the proposed FusionNetX model, and the results were substantially better, with gains of 0.3% to 1.9% across all datasets. Furthermore, the receiver operating characteristic curve is computed, and results from various performance evaluation metrics indicate that the proposed FusionNetX model can accurately detect and predict brain tumors and help health practitioners make timely decisions.