AI-based tool for early detection of Alzheimer's disease

Shafiq Ul Rehman; Noha Tarek; Caroline Magdy; Mohammed Kamel; Mohammed Abdelhalim; Alaa Melek; Lamees N Mahmoud; Ibrahim Sadek

doi:10.1016/j.heliyon.2024.e29375

AI-based tool for early detection of Alzheimer's disease

Heliyon. 2024 Apr 9;10(8):e29375. doi: 10.1016/j.heliyon.2024.e29375. eCollection 2024 Apr 30.

Authors

Shafiq Ul Rehman¹, Noha Tarek², Caroline Magdy², Mohammed Kamel², Mohammed Abdelhalim², Alaa Melek², Lamees N Mahmoud³, Ibrahim Sadek³

Affiliations

¹ College of Information Technology, Kingdom University, Bahrain.
² Systems and Biomedical Engineering, Faculty of Engineering, Cairo University, Cairo, Egypt.
³ Biomedical Engineering Dept, Faculty of Engineering, Helwan University, Helwan, Cairo, Egypt.

Abstract

In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease. Employing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our model undergoes training enriched by advanced image preprocessing techniques, achieving outstanding accuracy (testing 98.17 %, validation 97.52 %, training 99.62 %). The strategic use of transfer learning fortifies our competitive edge, incorporating the hippocampus approach and, notably, a progressive data augmentation technique. This innovative augmentation strategy gradually introduces augmentation factors during training, significantly elevating accuracy and enhancing the model's generalization ability. The study emphasizes practical application with a user-friendly website, empowering radiologists to predict class probabilities, track disease progression, and visualize patient images in both 2D and 3D formats, contributing significantly to the advancement of early AD detection.

Keywords: ADNI; Alzheimer's disease; Cognitively normal; Hippocampus; Image registration; Mild cognitive impairment; Skull Striping; Transfer learning; VGG16.