# A Practical Multiclass Classification Network for the Diagnosis of Alzheimer's Disease
###### Abstract
Some methods used support vector machine-based (SVM) classifiers with a mixed kernel approach [12] for the multimodal classification of AD and mild cognitive impairment. Some methods also used the combination of MRI biomarkers for volumetric measurement, hippocampal shape, and texture following the multiclass classification [13]. The recent methods proposed in the literature are becoming very popular for identifying and detecting Alzheimer's disease. By using these frameworks, researchers have recently conducted a plethora of experiments on the early identification of Alzheimer's disease using machine learning and deep learning technologies. Consequently, computer-assisted methods have been proposed for detecting Alzheimer's disease, particularly in individuals with severe dementia [14]. The computer-assisted smart system can significantly improve the detection without the involvement of the neuro-physicians [15]. The fundamental problem of image classification is based on the network's training on the labelled data. Next, the network is asked to predict the novel test sample of various categories for the accuracy of predictions. The classification problem is further categorized as binary and multiclass classification [16]. This work proposes a practical multiclass classification network for the primitive diagnosis of Alzheimer's disease (PMCAD-Net). Figure 1: The workflow of the proposed architecture. MRI scans are pre-processed with data cleaning operations for the train-test split to feed in the network for multiclass classification. The workflow of the proposed framework is shown in Figure 2. It demonstrates the whole pipeline ranging from the collection principle of capturing the MRI images with intermediate processing stages to multiclass classification. The proposed method is capable of handling the challenges of data shortages, as we apply a data augmentation strategy to resolve the challenges. The 3D MRI images obtained from the ADNI data repository are pre-processed and converted into 2D images. Data cleaning operations are performed, including skull striping normalization, registration, and segmentation. The proposed multiclass classification network avoids the negative transfer learning challenges faced by the previous methods and gets trained from scratch. It is important to note that the dataset in the image classification tasks are typically very large, whereas it is a complicated task to obtain a sufficiently large-scale dataset in the target domain. The data augmentations improve the data diversity. The operation of random cropping, rescaling, flipping and brightness changes are employed to obtain sufficiently large-scale generalized data as shown in the first part of Figure 2. These operations improve the learning capabilities of the network shown in the same figure for key aspects of the input data classes. Extensive experiments are conducted for the performance evaluation of the proposed network. The overall comparison demonstrates that our method outperformed the state-of-the-art approaches. Figure 2: Framework of the proposed framework, with several pre-processing steps, and detailed architecture of the practical multiclass classification network for the diagnosis of Alzheimer’s disease (PMCAD-Net). This work focus on multiclass classification, where we also resolve the challenges of the data shortage. There are several problems, including perspective fluctuation, size, scale, class, and data variation [17]. The extant methods in the literature rely on the pre-trained models and suffer several problems, including computational complexity and layer freezing.