Age Detection from Brain MRI Images Using the Deep Learning

Abstract

Estimating the age of the brains of individuals from brain images can be very useful in many applications. The brain’s age has greatly contributed to predicting and preventing early deaths in the medical community. It can also be very useful for diagnosing diseases, such as Alzheimer’s. According to the authors knowledge, this paper is one of the first researches that have been done in age detection by brain images using Deep Learning (DL). In this paper, the convolution neural network (CNN), used for age detection from brain magnetic resonance images (MRI). The images used in this paper are from the imaging centers and collected by the author of the paper. In this paper 1290 images have been collected, 941 images for train data and 349 images for test images. Images collected at the centers were labeled age. In this paper, the Alexnet model is used in CNN architecture. The used architecture of the architecture has 5 Convolutional layers and 3 Sub-sampling layers that the last layer has been used to categorize the image. The CNN that the last layer has been used to categorize the images into five age classes.The accuracy of the CNN is obtained by the Softmax classifier 79%, Support Vector Machine (SVM) classifier 75% and the Decision Tree (DT) classifier, 49%. In addition to the accuracy criterion, we use the benchmarks of Recall, Precision and F1-Score to evaluate network performance.