Blockchain Integrated Neural Networks: A New Frontier in MRI-based Brain Tumor Detection

Brain tumors originating from uncontrolled growth of abnormal cells in the brain, presents a significant challenge in healthcare due to their various symptoms and infrequency. While Magnetic Resonance Imaging (MRI) is essential for accurately identifying and diagnosing malignant tumors, manual interpretation is often complex and sensitive to mistakes. To address this, we introduce BrainTumorNet, a specialized convolutional neural network (CNN) created for MRI-based brain tumor diagnosis. We ensure improved image quality and a robust dataset for model training by including preprocessing approaches involving CLAHE and data augmentation. Additionally, we integrated a blockchain-based data retrieval technology to enhance the security, traceability, and collaboration in MRI data management across several medical institutions. This blockchain framework ensures that MRI data, once input from hospitals, stays immutable and can be safely retrieved based on unique hospital IDs, promoting a trustable environment for data exchange. Performance assessments conducted on multiple MRI datasets showcased BrainTumorNet's commendable proficiency, with accuracy rates of 98.66%, 97.17% and 94.24% on the dataset 1, dataset 2, and dataset 3, respectively. The model's performance was evaluated using a comprehensive set of metrics, including accuracy, specificity, recall, precision, f1-score, and confusion matrix. These measures are essential for evaluating a model's strengths and limits, emphasizing BrainTumorNet's ability to generate accurate and relevant predictions and its effectiveness in determining negative classification. BrainTumorNet's performance was compared with six renowned deep learning InceptionV3, and DenseNet121. Our work highlights BrainTumorNet's potential capabilities in simplifying and boosting the accuracy of MRI-based brain tumor diagnosis while ensuring data integrity and collaboration through blockchain.