Automated prediction system for Alzheimer detection based on deep residual autoencoder and support vector machine

Menagadevi, M.; Mangai, S.; Nirmala, M.; Thiyagarajan, D.

doi:10.1016/j.ijleo.2022.170212

Cited by 26 publications

(14 citation statements)

References 52 publications

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“…In recent years, techniques employing deep learning to diagnose AD have gained prominence [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Deep learning is a type of machine learning that is particularly well suited for the analysis of complex medical images, as it has the ability to automatically learn and extract features from large datasets.…”

Section: Related Workmentioning

confidence: 99%

“…Several different approaches have been used to develop deep learning models for AD diagnosis using MRI images. Menagadevi et al [ 8 ] developed a computer-aided diagnosis system for detecting AD based on a combination of a deep learning model with traditional classification methods. They first start with preprocessing stages on the input MRI images to enhance the images.…”

Section: Related Workmentioning

confidence: 99%

“…They obtained an overall accuracy of 95.23% for the multi-classification task. However, similar to Menagadevi et al [ 8 ], the study only focused on MRI imaging and used small datasets. Loddo et al [ 10 ] presented a fully automatic model based on ensemble deep learning approaches for diagnosing AD from MRI images.…”

Section: Related Workmentioning

confidence: 99%

“…These can include medications to improve cognitive function, therapies to address behavioral and psychological symptoms, and support for caregivers. Research is ongoing to develop more effective treatments and to better understand the underlying causes of AD [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. In recent years, deep learning techniques have been successfully applied to a wide range of medical imaging tasks [ 24 , 25 , 26 , 27 , 28 ], including the detection of AD [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…Research is ongoing to develop more effective treatments and to better understand the underlying causes of AD [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. In recent years, deep learning techniques have been successfully applied to a wide range of medical imaging tasks [ 24 , 25 , 26 , 27 , 28 ], including the detection of AD [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. These techniques have the ability to automatically learn and extract features from large datasets, making them well suited for the analysis of complex medical images.…”

Section: Introductionmentioning

confidence: 99%

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Accurate Detection of Alzheimer’s Disease Using Lightweight Deep Learning Model on MRI Data

et al. 2023

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Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interventions, as the disease is more responsive to treatment in its early stages. It is worth mentioning that deep learning techniques have been successfully applied in recent years to a wide range of medical imaging tasks, including the detection of AD. These techniques have the ability to automatically learn and extract features from large datasets, making them well suited for the analysis of complex medical images. In this paper, we propose an improved lightweight deep learning model for the accurate detection of AD from magnetic resonance imaging (MRI) images. Our proposed model achieves high detection performance without the need for deeper layers and eliminates the use of traditional methods such as feature extraction and classification by combining them all into one stage. Furthermore, our proposed method consists of only seven layers, making the system less complex than other previous deep models and less time-consuming to process. We evaluate our proposed model using a publicly available Kaggle dataset, which contains a large number of records in a small dataset size of only 36 Megabytes. Our model achieved an overall accuracy of 99.22% for binary classification and 95.93% for multi-classification tasks, which outperformed other previous models. Our study is the first to combine all methods used in the publicly available Kaggle dataset for AD detection, enabling researchers to work on a dataset with new challenges. Our findings show the effectiveness of our lightweight deep learning framework to achieve high accuracy in the classification of AD.

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