AD-ResNet50: An Ensemble Deep Transfer Learning and SMOTE Model for Classification of Alzheimer’s Disease

Today, one of the emerging challenges faced by neurologists is to categorize Alzheimer's disease (AD). It is a type of neurodegenerative disorder and leads to progressive mental loss and is known as Alzheimer's disease (AD) (Tanveer et al. in Commun Appl 16:1–35, 2020). An immediate diagnosis of Alzheimer's disease is one of the requirements and developing an effective treatment strategy and stopping the disease’s progression. Resonance magnetic imaging (MRI) and CT scans can enable local changes in brain structure and quantify disease-related damage. The standard machine learning algorithms are designed to detect AD to have poor performance because they were trained using insufficient sample data. In comparison with traditional machine learning algorithms, deep learning models have shown superior performance in most of the research studies stated specific to diagnosis of AD. One of the elegant DL method is the convolutional neural network (CNN) and has helped to assist the early diagnosis of AD (Sethi et al. in BioMed Research International, 2022; Islam and Zhang in Proceedings IEEE/CVF 841 conference computing vision pattern recognition workshops (CVPRW), pp 1881–1883, 2018). However, in recent days advanced DL methods have also attempted for classification of AD, especially in MRI images (Tiwari et al. in Int J Nanomed 14:5541, 2019). The purpose of this paper is to propose a ResNet50 model for Alzheimer's disease, namely AD-ResNet50 for MRI images that incorporates two extensions known as transfer learning and SMOTE. This research uses the proposed method and compares it with the standard deep models VGG19, InceptionResNet V2, and DenseNet169 with transfer learning and SMOTE (Chawla et al. in J Artif Intell Res 16:(1)321–357, 2002). The results demonstrate the efficiency of the proposed method, which outperforms the other three models tested. When compared with baseline deep learning models, the proposed model outperformed them in terms of accuracy and ROC values.