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Blockchain-Based Deep Learning Approach for Alzheimer's Disease Classification
Abstract
Blockchain is an emerging technology that is now being used to provide novel solutions in several industries, including healthcare. Deep learning (DL) algorithms have grown in popularity in medical image processing research. AD is diagnosed by magnetic resonance imaging (MRI) images. This study investigates the integration of blockchain technology with a DL model for Alzheimer's disease prediction (AD). This proposed model was used to classify 3182 images from the ADNI collection. The edge-based segmentation algorithm has overcome the segmentation problem. During the investigation's test stage, the DL-EfficientNetB0 model with blockchain earned the highest accuracy rate of 99.14%. The highest accuracy, sensitivity, and specificity scores were obtained utilizing the confusion matrix during the comparative assessment stage. According to the study's results, EfficientNetB0 with blockchain model surpassed all other trained models in classification rate. This study will aid clinical research into the early detection and prevention of AD by identifying the sickness before it occurs.
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