# A Practical Multiclass Classification Network for the Diagnosis of Alzheimer's Disease
## 2 Related Work  
Alzheimer's disease is one of the most common kinds of neurological dementia, resulting in various memory-related neurological disorders. According to the 2015 World Alzheimer's Report, around 50 million people worldwide have dementia, with Alzheimer's disease accounting for 70-80% of cases [18]. Alzheimer's disease will impact 131.5 million people globally by the year 2050 according to projections [19]. The number of AD patients is increasing day by day, and it is one of the primary causes of death among older adults. The extant methods for the detection of the AD are heavily dependent upon neurological scans and high-dimensional data, including several imaging modalities such as MRI, functional MRI (fMRI), positron emission tomography (PET), amyloid-PET, and diffusion tensor imaging. However, due to nuance in patterns, it is still very challenging to distinguish between the patterns with radiological reading. Thus, it challenging to diagnose AD at an early stage. Recently, several methods have been proposed for image enhancement [20; 21; 22] based on machine learning, deep learning and few shot learning [23]. These feature extraction- and classification-based approaches [24; 25] are commonly used to design predictive models for intelligent and expert system-based applications [9; 26]. Based on MRI, a multi-modal framework is proposed to extract neurological features with a consistent metric constraint for the classification of various dementias [27]. A deep Siamese convolution neural network (SCNN) for the multiclass classification of AD is proposed in [28] to classify various stages of the disease. A natural image-based network to represent neuroimaging data (NIBR-Net) is a significant approach in the target domain, based on sparse autoencoder [29], where the network learns from a set of bases from natural images with the help of convolution to extract features from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Another deep sparse multi-task learning method, along with a feature-adaptive weighting scheme for feature selection in AD (DSMAD-Net), is proposed in [30]. It selects the useful features in a single hierarchy and iteratively filters out undesired features hierarchically. It also uses regression coefficients as context information and reflects the complex distributional characteristics in each class. In a similar fashion, a deep learning-based 4-way multiclass classifier (DLMCC-Net) is proposed in [31]. This framework follows some extant frameworks such as Google-net architecture [32] for the multiclass classification of various stages of the disease. A network-based transfer learning approach for the early diagnosis of AD (TLEDA-Net) is proposed in [33], and another Alzheimer's disease classification using transfer learning (ADTL-Net) is proposed in [34]. At the same time, the class imbalance issue is handled in [35] with a DEMNET by using the pre-processed Kaggle dataset. Another convolutional neural network-based Alzheimer's disease classification from MRI brain data (CNN-AD) is proposed in [7]. The earlier techniques for multiclass classification were developed in the target domain with a substantial gradient flow strategy to improve the performance [36]. Using three-dimensional image features, a Sobolev gradient-based optimization approach was proposed for the accurate diagnosis of AD following a 3D convolutional network (SGO-3D) [37]. The authors of [38] proposed a neuroimaging study based on a 3D convolutional neural network for predicting Alzheimer's disease (3D-CNN-PAD). Another multiclass classification method in [39] employed CNN architecture following a transfer learning-based framework for the AD. The authors of [40] developed a 3D deeply supervised adaptable convolutional neural network (CNN-3D) to predict AD. This framework can work without the skull striping, such that the CNN-3D can learn the generic features capturing AD bio-markers.