Feed-forward LPQNet based Automatic Alzheimer's Disease Detection Model

Ela Kaplan; Sengul Dogan; Turker Tuncer; Mehmet Baygin; Erman Altunisik

doi:10.1016/j.compbiomed.2021.104828

Feed-forward LPQNet based Automatic Alzheimer's Disease Detection Model

Comput Biol Med. 2021 Oct:137:104828. doi: 10.1016/j.compbiomed.2021.104828. Epub 2021 Sep 4.

Authors

Ela Kaplan¹, Sengul Dogan², Turker Tuncer³, Mehmet Baygin⁴, Erman Altunisik⁵

Affiliations

¹ Department of Radiology, Adiyaman Training and Research Hospital, Adiyaman, Turkey. Electronic address: elakaplan15@gmail.com.
² Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey. Electronic address: sdogan@firat.edu.tr.
³ Department of Digital Forensics Engineering, College of Technology, Firat University, Elazig, Turkey. Electronic address: turkertuncer@firat.edu.tr.
⁴ Department of Computer Engineering, College of Engineering, Ardahan University, Ardahan, Turkey. Electronic address: mehmetbaygin@ardahan.edu.tr.
⁵ Department of Neurology, Adiyaman University Medicine Faculty, Adiyaman, Turkey. Electronic address: ealtunisik@adiyaman.edu.tr.

PMID: 34507154
DOI: 10.1016/j.compbiomed.2021.104828

Abstract

Background: Alzheimer's disease (AD) is one of the most commonly seen brain ailments worldwide. Therefore, many researches have been presented about AD detection and cure. In addition, machine learning models have also been proposed to detect AD promptly.

Materials and method: In this work, a new brain image dataset was collected. This dataset contains two categories, and these categories are healthy and AD. This dataset was collected from 1070 subjects. This work presents an automatic AD detection model to detect AD using brain images automatically. The presented model is called a feed-forward local phase quantization network (LPQNet). LPQNet consists of (i) multilevel feature generation based on LPQ and average pooling, (ii) feature selection using neighborhood component analysis (NCA), and (iii) classification phases. The prime objective of the presented LPQNet is to reach high accuracy with low computational complexity. LPQNet generates features on six levels. Therefore, 256 × 6 = 1536 features are generated from an image, and the most important 256 out 1536 features are selected. The selected 256 features are classified on the conventional classifiers to denote the classification capability of the generated and selected features by LPQNet.

Results: The presented LPQNet was tested on three image datasets to demonstrate the universal classification ability of the LPQNet. The proposed LPQNet attained 99.68%, 100%, and 99.64% classification accuracy on the collected AD image dataset, the Harvard Brain Atlas AD dataset, and the Kaggle AD dataset. Moreover, LPQNet attained 99.62% accuracy on the Kaggle AD dataset using four classes.

Conclusions: Moreover, the calculated results from LPQNet are compared to other automatic AD detection models. Comparisons, results, and findings clearly denote the superiority of the presented model. In addition, a new intelligent AD detector application can be developed for use in magnetic resonance (MR) and computed tomography (CT) devices. By using the developed automated AD detector, new generation intelligence MR and CT devices can be developed.

Keywords: Alzheimer's disease detection; Classification; Feature selection; LPQNet; NCA; Textural feature generation.

MeSH terms

Alzheimer Disease* / diagnostic imaging
Brain / diagnostic imaging
Humans
Machine Learning
Magnetic Resonance Imaging