Advanced Estimation of Brain Age Using Pre-trained 2D Convolutional Neural Networks on a Public Dataset - Al-kubaisi, Mohannad; Ahmed, Ali Saadoon; Al-Barzinji, Shokhan M.; Khaleel, Arshad M. (Journal of Robotics and Control (JRC), 2024)
This work introduces a brand-new approach to be employed for correctly assessing healthy person’s brain aging, masking what constitutes the most serious challenge in the fight against age-related cognitive decline. An approach is serviced by 2D CNNs, a simpler technology to state-of-the-art 3D model, to yield close to accurate forecast. Our algorithm improves telling in two respects. By virtue of this, we will utilize well-known ImageNet-pre-trained classifiers to suggest initial brain age predictions. This process sets the tone of the core of our business model in terms of dependability and reliability. Next, we improve the networks’ performance through progressively expanding their capacity via fine-tuning on pre-segmentation tasks using the neuroimaging datasets which are openly available. This stage increases the predictive accuracy in addition to ensuring that there is transparency and flexibility because it utilizes open datasets. Our research's strength is that it encompasses all techniques and fields necessary for brain age estimation and adopts justifiable evaluation metrics. As a result, this conduct adds to the literature. Our study not only points out deficiencies in private datasets but reels out the validity of our approach by using the public data instead to give the results another direction of accessibility and reproducibility.