Early Detection of Dementia using Deep Learning and Image Processing

Patil, Basavaraj Mali; Raigonda, Megha Rani; Anakal, Sudhir; Bhadrashetty, Ambresh

doi:10.5815/ijem.2023.01.02

Cited by 4 publications

(2 citation statements)

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“…The main purposes of image blurring is to give the image preprocessing to reduce the image noise. For example, to remove some trivial details from the image before the extraction of large targets ( 30 ).The image acquisition may be unclear due to the shooting distance, camera movement, and camera focus, which also affects the detection results of the neural network. Therefore, random blurring is applied to images enhanced by luminance and rotation to improve the robustness of the data.…”

Section: Methodsmentioning

confidence: 99%

YOLOv5s-gnConv: detecting personal protective equipment for workers at height

Chen,

Li,

Wen

et al. 2023

Front. Public Health

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IntroductionFalls from height (FFH) accidents can devastate families and individuals. Currently, the best way to prevent falls from heights is to wear personal protective equipment (PPE). However, traditional manual checking methods for safety hazards are inefficient and difficult to detect and eliminate potential risks.MethodsTo better detect whether a person working at height is wearing PPE or not, this paper first applies field research and Python crawling techniques to create a dataset of people working at height, extends the dataset to 10,000 images through data enhancement (brightness, rotation, blurring, and Moica), and categorizes the dataset into a training set, a validation set, and a test set according to the ratio of 7:2:1. In this study, three improved YOLOv5s models are proposed for detecting PPE in construction sites with many open-air operations, complex construction scenarios, and frequent personnel changes. Among them, YOLOv5s-gnconv is wholly based on the convolutional structure, which achieves effective modeling of higher-order spatial interactions through gated convolution (gnConv) and cyclic design, improves the performance of the algorithm, and increases the expressiveness of the model while reducing the network parameters.ResultsExperimental results show that YOLOv5s-gnconv outperforms the official model YOLOv5s by 5.01%, 4.72%, and 4.26% in precision, recall, and mAP_0.5, respectively. It better ensures the safety of workers working at height.DiscussionTo deploy the YOLOv5s-gnConv model in a construction site environment and to effectively monitor and manage the safety of workers at height, we also discuss the impacts and potential limitations of lighting conditions, camera angles, and worker movement patterns.

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Section: Methodsmentioning

confidence: 99%

YOLOv5s-gnConv: detecting personal protective equipment for workers at height

Chen,

Li,

Wen

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

show abstract

“…Their strategy joined progressed neural arrange structures and accomplished competitive execution in terms of symptomatic accuracy. Basavaraj et al (2023) [23] explored the early location of dementia utilizing profound learning and picture-handling procedures. Their consideration centred on identifying unobtrusive basic changes in the brain related to dementia utilizing MRI data.…”

Section: Related Workmentioning

confidence: 99%

Application of Convolutional Neural Networks for Early Detection and Classification of Alzheimer's disease from MRI Images

Suman Kumar Swarnkar

2024

jes

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This study investigates the application of convolutional neural networks (CNNs) and traditional machine learning algorithms for the early detection and classification of Alzheimer's disease (AD) using brain Magnetic Resonance Imaging (MRI) data. We compare the performance of CNNs with Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM) on a dataset comprising MRI images from AD patients and healthy controls. Results show that CNNs achieved the highest accuracy (90.2%) and area under the receiver operating characteristic curve (AUC-ROC) of 0.95, outperforming SVM, RF, and GBM. The CNN model also exhibited high sensitivity (87.5%) and specificity (92.6%) in distinguishing between AD patients and healthy controls. These findings highlight the effectiveness of CNN-based approaches in leveraging raw MRI images for accurate and early detection of AD.

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Waste Classification Using Random Forest Classifier with DenseNet201 Deep Features

Akyol,

Karacı

2023

Lecture Notes on Data Engineering and Communications Technologies

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Early Detection of Dementia using Deep Learning and Image Processing

Cited by 4 publications

References 0 publications

YOLOv5s-gnConv: detecting personal protective equipment for workers at height

YOLOv5s-gnConv: detecting personal protective equipment for workers at height

Application of Convolutional Neural Networks for Early Detection and Classification of Alzheimer's disease from MRI Images

Waste Classification Using Random Forest Classifier with DenseNet201 Deep Features

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