Brain Ageing Prediction using Isolation Forest Technique and Residual Neural Network (ResNet)

TL;DR

This paper introduces a deep learning approach using ResNet101V2 and Isolation Forest for accurate brain age prediction from MRI scans, demonstrating high performance on a large dataset and highlighting the importance of outlier detection.

Contribution

The study presents a novel combination of ResNet101V2 and Isolation Forest techniques for improved brain age estimation from neuroimaging data.

Findings

ResNet101V2 outperformed other models in brain age prediction.

Isolation Forest improved prediction accuracy by reducing outliers.

Achieved MAEs of approximately 0.82-0.91 years on the ICBM dataset.

Abstract

Brain aging is a complex and dynamic process, leading to functional and structural changes in the brain. These changes could lead to the increased risk of neurodegenerative diseases and cognitive decline. Accurate brain-age estimation utilizing neuroimaging data has become necessary for detecting initial signs of neurodegeneration. Here, we propose a novel deep learning approach using the Residual Neural Network 101 Version 2 (ResNet101V2) model to predict brain age from MRI scans. To train, validate and test our proposed model, we used a large dataset of 2102 images which were selected randomly from the International Consortium for Brain Mapping (ICBM). Next, we applied data preprocessing techniques, including normalizing the images and using outlier detection via Isolation Forest method. Then, we evaluated various pre-trained approaches (namely: MobileNetV2, ResNet50V2, ResNet101V2, Xception). The results demonstrated that the ResNet101V2 model has higher performance compared with the other models, attaining MAEs of 0.9136 and 0.8242 years for before and after using Isolation Forest process. Our method achieved a high accuracy in brain age estimation in ICBM dataset and it provides a reliable brain age prediction.