A Practical Multiclass Classification Network for the Diagnosis of Alzheimer's Disease

Patients who have Alzheimer's disease (AD) pass through several irreversible stages, which ultimately result in the patient's death. It is crucial to understand and detect AD at an early stage to slow down its progression due to the non-curable nature of the disease. Diagnostic techniques are primarily based on magnetic resonance imaging (MRI) and expensive high-dimensional 3D imaging data. Classic methods can hardly discriminate among the almost similar pixels of the brain patterns of various age groups. The recent deep learning-based methods can contribute to the detection of the various stages of AD but require large-scale datasets and face several challenges while using the 3D volumes directly. The extant deep learning-based work is mainly focused on binary classification, but it is challenging to detect multiple stages with these methods. In this work, we propose a deep learning-based multiclass classification method to distinguish amongst various stages for the early diagnosis of Alzheimer's. The proposed method significantly handles data shortage challenges by augmentation and manages to classify the 2D images obtained after the efficient pre-processing of the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our method achieves an accuracy of 98.9% with an F1 score of 96.3. Extensive experiments are performed, and overall results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of overall performance.

Automated summary

Patients with Alzheimer's disease go through irreversible stages, and early detection is crucial for disease progression. Diagnostic techniques rely on MRI and high-dimensional 3D imaging data. Deep learning-based methods can help detect different stages of AD, but face challenges with 3D volumes. This study proposes a deep learning-based multiclass classification method for early diagnosis of Alzheimer's, achieving high accuracy and outperforming existing methods.