Alzheimer's disease Classification from Brain MRI based on transfer learning from CNN

• Published in: 2018 11th Biomedical Engineering International Conference (BMEiCON) pp. 1 - 4
• Main Authors: Khagi, Bijen, Lee, Chung Ghiu, Kwon, Goo-Rak
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2018
• Subjects: Alzheimer's disease; Biomedical imaging; Classifier; CNN; Convolutional neural networks; Feature extraction; generic feature; Magnetic resonance imaging; MRI; PCA; Training; TSNE
• DOI: 10.1109/BMEiCON.2018.8609974

Various Convolutional Neural Network (CNN) architecture has been proposed for image classification and Object recognition. For the image based classification, it is a complex task for CNN to deal with hundreds of MRI Image slices, each of almost identical nature in a single patient. So, classifying a number of patients as an AD, MCI or NC based on 3D MRI becomes vague technique using 2D CNN architecture. Hence, to address this issue, we have simplified the idea of classifying patients on basis of 3D MRI but acknowledging the 2D features generated from the CNN framework. We present our idea regarding how to obtain 2D features from MRI and transform it to be applicable to classify using machine learning algorithm. Our experiment shows the result of classifying 3 class subjects patients. We employed scratched trained CNN or pretrained Alexnet CNN as generic feature extractor of 2D image which dimensions were reduced using PCA+TSNE, and finally classifying using simple Machine learning algorithm like KNN, Navies Bayes Classifier. Although the result is not so impressive but it definitely shows that this can be better than scratch trained CNN softmax classification based on probability score. The generated feature can be well manipulated and refined for better accuracy, sensitivity, and specificity.