A Novel Framework for Classification of Different Alzheimer’s Disease Stages Using CNN Model

Abstract

Background: Alzheimer’s, the predominant formof dementia, is a neurodegenerative brain disorder with no known cure. With the lack of innovative findings to diagnose and treat Alzheimer’s, the number of middle-aged people with dementia is estimated to hike nearly to 13 million by the end of 2050. The estimated cost of Alzheimer’s and other related ailments is USD321 billion in 2022 and can rise above USD1 trillion by the end of 2050. Therefore, the early prediction of such diseases using computer-aided systems is a topic of considerable interest and substantial study among scholars. The major objective is to develop a comprehensive framework for the earliest onset and categorization of different phases of Alzheimer’s. Methods: Experimental work of this novel approach is performed by implementing neural networks (CNN) on MRI image datasets. Five classes of Alzheimer’s disease subjects are multi-classified. We used the transfer learning determinant to reap the benefits of pre-trained health data classification models such as the MobileNet. Results: For the evaluation and comparison of the proposed model, various performance metrics are used. The test results reveal that the CNN architectures method has the following characteristics: appropriate simple structures that mitigate computational burden, memory usage, and overfitting, as well as offering maintainable time. The MobileNet pre-trained model has been fine-tuned and has achieved 96.6 percent accuracy for multi-class AD stage classifications. Other models, such as VGG16 and ResNet50 models, are applied tothe same dataset whileconducting this research, and it is revealed that this model yields better results than other models. Conclusion: The study develops a novel framework for the identification of different AD stages. The main advantage of this novel approach is the creation of lightweight neural networks. MobileNet model is mostly used for mobile applications and was rarely used for medical image analysis; hence, we implemented this model for disease detection andyieldedbetter results than existing models.

A novel approach to recognition of Alzheimer’s and Parkinson’s diseases: random subspace ensemble classifier based on deep hybrid features with a super-resolution image

“…Like the current study, the remaining studies are based on the PD, AD, and healthy control classification, and according to the accuracies obtained from previous studies, AlSaeed & Omar (2022) achieved the best performance for AD and healthy control classification with an accuracy score of 99%. In other studies ( Helaly, Badawy & Haikal, 2022 ; Bhagat et al, 2023 ; Noella & Priyadarshini, 2023a ; Alsharabi et al, 2023 ), a performance of 95% and above was achieved, whereas in the remaining studies, accuracy scores between %85-90 were obtained. Based on all these results, the proposed model for classifying PD, AD, and healthy control has produced superior performance over previous studies.…”

Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection

“…Table 7 compares the methods currently utilized for predicting AD. To enhance the categorization of early AD phases while reducing parameters and computational costs, a novel detection network named DAD-Net was introduced by Mohi et al ( 2023 ). This network appropriately classified initial AD processes and depicted class activation characteristics as a heat map of the brain, achieving 99.2% accuracy using a Kaggle dataset.…”

“…Thus, researchers are improving medical image processing to identify AD correctly. This section presents relevant literature in the domain of AD detection and diagnosis, which focuses primarily on classification techniques based on deep learning for MRI tissue structure analysis (Mohi et al, 2023 ). The deep belief network (DBN) was utilized by AI-Atroshi et al ( 2022 ) to extract feature vectors from detected speech samples, which has an output accuracy of 90.2%.…”

Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test

“… Mohi ud din dar et al (2023) studied the ADNI dataset using T2-weighted MRI images. The dataset included data from 300 AD patients divided into five classes: CN, MCI, EMCI, LMCI, and AD.…”