A CNN Model: Earlier Diagnosis and Classification of Alzheimer Disease using MRI

Salehi, Ahmad Waleed; Baglat, Preety; Sharma, Bhanu; Gupta, Gaurav; Upadhya, Ankita

doi:10.1109/icosec49089.2020.9215402

Cited by 80 publications

(23 citation statements)

References 9 publications

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“…More importantly, security, any attacks on control, availability, and privacy can all be targets. Figure 5 described how digital technologies are used in the society and healthcare sector to enhance and improve the living quality of patients with different kinds of diseases [ 40 , 41 ], delivering good healthcare services, more personalized and precise medicine, and clinical support.…”

Section: Basics and Background: Iot Applications In Healthcare Industrymentioning

confidence: 99%

IoT‐Based Wearable Devices for Patients Suffering from Alzheimer Disease

Salehi

Gupta

Bhatia

et al. 2022

Contrast Media & Molecular Imaging

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The disorder of Alzheimer’s (AD) is defined as a gradual deterioration of cognitive functions, such as the failure of spatial cognition and short-term memory. Besides difficulties in memory, a person with this disease encounters visual processing difficulties and even awareness and identifying of their beloved ones. Nowadays, recent technologies made this possible to connect everything that exists around us on Earth through the Internet, this is what the Internet of Things (IoT) made possible which can capture and save a massive amount of data that are considered very important and useful information which then can be valuable in training of the various state-of-the-art machine and deep learning algorithms. Assistive mobile health applications and IoT-based wearable devices are helping and supporting the ongoing health screening of a patient with AD. In the early stages of AD, the wearable devices and IoT approach aim to keep AD patients mentally active in all of life’s daily activities, independent from their caregivers or any family member of the patient. These technological solutions have great potential in improving the quality of life of an AD patient as this helps to reduce pressure on healthcare and to minimize the operational cost. The purpose of this study is to explore the State-of-the-Art wearable technologies for people with AD. Significance, challenges, and limitations that arise and what will be the future of these technological solutions and their acceptance. Therefore, this study also provides the challenges and gaps in the current literature review and future directions for other researchers working in the area of developing wearable devices.

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Section: Basics and Background: Iot Applications In Healthcare Industrymentioning

confidence: 99%

IoT‐Based Wearable Devices for Patients Suffering from Alzheimer Disease

Salehi

Gupta

Bhatia

et al. 2022

Contrast Media & Molecular Imaging

Self Cite

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“…We consider that this kind of architecture is growing in use. We also continued to find research efforts that only report high accuracy, but not other metrics [ 24 , 25 , 26 , 27 , 28 ]. Thus, the paradigm of obtaining the highest possible accuracy in classification remains.…”

Section: Previous Workmentioning

confidence: 99%

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

Solano-Rojas

Ricardo

2021

Sensors

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Alzheimer’s disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning for those potentially affected. This paper uses a three-dimensional DenseNet architecture to detect Alzheimer’s disease in magnetic resonance imaging. Our work is restricted to the use of freely available tools. We constructed a deep neural network classifier with metrics of 0.86¯ mean accuracy, 0.86¯ mean sensitivity (micro-average), 0.86¯ mean specificity (micro-average), and 0.91¯ area under the receiver operating characteristic curve (micro-average) for the task of discriminating between five different disease stages or classes. The use of tools available for free ensures the reproducibility of the study and the applicability of the classification system in developing countries.

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“…Contrast enhancement is a technique for making visual characteristics stand out more clearly by making good use of the display's colors or output device. Later noise is removed using median filter [11,12]. The steps involved are shown in Figure 3.…”

Section: Image Pre-processingmentioning

confidence: 99%

A Deep Learning-Based Approach for Detection of Dementia from Brain Mri

L¹,

Nalluri²,

Sankpal³

2021

JUSST

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In this paper, a user-friendly system has been developed which will provide the result of medical analysis of digital images like magnetization resonance of image scan of the brain for detection and classification of dementia. The small structural differences in the brain can slowly and gradually become a major disease like dementia. The progression of dementia can be slowed when identified early. Hence, this paper aims at developing a robust system for classification and identifying dementia at the earliest. The method used in this paper for initial disclosure and diagnosis of dementia is deep learning since it can give important results in a shorter period of time. Deep Learning methods such as K-means clustering, Pattern Recognition, and Multi-class Support Vector Machine (SVM) have been used to classify different stages of dementia. The goal of this study is to provide a user interface for deep learning-based dementia classification using brain magnetic resonance imaging data. The results show that the created method has an accuracy of 96% and may be utilized to detect people who have dementia or are in the early stages of dementia.

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A CNN Model: Earlier Diagnosis and Classification of Alzheimer Disease using MRI

Cited by 80 publications

References 9 publications

IoT‐Based Wearable Devices for Patients Suffering from Alzheimer Disease

IoT‐Based Wearable Devices for Patients Suffering from Alzheimer Disease

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

A Deep Learning-Based Approach for Detection of Dementia from Brain Mri

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