Comparative analysis of Alzheimer's disease classification by CDR level using CNN, feature selection, and machine‐learning techniques. Issue 3 (7th March 2019)

• Record Type: Journal Article
• Authors: Khagi, Bijen; Kwon, Goo‐Rak; Lama, Ramesh
• Is Part Of: International journal of imaging systems and technology. Volume 29:Issue 3(2019)
• Page Start: 297
• Page End: 310
• Publication Date: 2019-03-07
• Subjects: Alexnet -- automatic features -- CNN -- feature ranking -- KNN -- machine learning -- manual features -- SVM; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22316
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Magnetic resonance imaging (MRI) of brain needs an impeccable analysis to investigate all its structure and pattern. This analysis may be a sharp visual analysis by an experienced medical professional or by a computer aided diagnosis system that can help to predict, what may be the recent condition. Similarly, on the basis of various information and technique, a system can be designed to detect whether a patient is prone to Alzheimer's disease or not. And this task of detection of abnormalities at an initial stage from brain MRI is a major challenge in the field of neurosciences. The main idea behind our research is to utilize the deep layers feature extraction benefited from deep neural network architecture, without extensive hardware resource training, and classifying the image on a basis of simple machine‐learning algorithm with selected best features in order to reduce work load, classification error and hardware utilization time. We have utilized convolution neural network (CNN) layer using similar architecture like that of Alexnet with some parametric change, for the automatic extraction of features of images obtained from slice extraction of whole brain MRI whereas 13 manual features based on gray level co‐occurrence matrix were also extracted to test the impact of this features on ranking. If we had only classified using CNN network, the misclassification rate was much higher. So, feature selection is achieved with feature ranking algorithms like Mutinffs, ReliefF, Laplacian and UDFS and so on and also tested with different machine‐learning techniques like Support Vector Machine, K‐Nearest Neighbor and Subspace Ensemble under different testing condition. The performance of the result is satisfactory with classification accuracy around 98% to 99% with 7:3 ratio of random holdout partition of training to testing image sets and also with fivefolds of cross‐validation on the same set using a standardized template.