# A Practical Multiclass Classification Network for the Diagnosis of Alzheimer's Disease
###### Abstract  
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. Alzheimer's disease; multiclass classification; deep learning Article  
A Practical Multiclass Classification Network for the Diagnosis of Alzheimer's Disease  
1  
[1]School of Computer Science and Mathematics, Zhejiang Normal University, Jinhua 321004, China;  
[EMAIL_ADDRESS]  
Z.H. The progression of AD is categorized into various stages ranging from normal control (NC) to early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI). It is a complicated task to determine the normal brain cells with the MRI scans and distinguish among various stages of Alzheimer's [4,5]. Current high-dimensional diagnostic techniques generate 3D MRI images with thousands of voxels in a single image. The direct use of these images in the convolutional neural network raises the challenges of computational complexity and data management [6]. One of the major challenges in the target domain is the unavailability of the large-scale balanced dataset, whereas the deep learning-based framework is data-hungry and is heavily dependent on the large-scale input training data [7]. It can be observed in Figure 1, where the working principle for capturing MRI images is shown, that skull stripping and slice extraction are the main issues while feeding the data samples to the neural network. In the absence of large-scale datasets, learning distinctive features in brain images with similar pixel intensities is challenging for multi-scale classification. The discriminative learning of the existing binary classification methods are hardly applicable for the multi-level classification; therefore, new methods are imperative to handle these challenges [8]. The capacities of the traditional methods are limited and might be prone to several artifacts due to the similar brain pattern and pixel intensity of the images. The recent advancements in machine learning and deep learning provide a unique way to distinguish such brain scans. The convolutional neural networks (CNNs) can automatically detect and distinguish among the various pattern and play a vital role in computer vision and medical image analysis [9,10]. Thus, deep learning approaches contribute to predicting the above-mentioned stages of AD. The authors of [11] suggested a pairwise similarity analysis for each modality based on the multimodal framework by using non-linear graph fusion for distinct modalities for classification.