A Novel Deep Dense Block-Based Model for Detecting Alzheimer’s Disease

Çelebi, Selahattin Barış

doi:10.3390/app13158686

Cited by 5 publications

(4 citation statements)

References 54 publications

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“…In 28 , a 3D CNN was designed to distinguish between AD and CN using resting-state fMRI images. Meanwhile, Çelebi et al 29 utilized morphometric images from Tensor-Based Morphometry (TBM) preprocessing of MRI data. Their study employed the deep, dense block-based Xception architecture-based DL method, achieving high accuracy in early-stage Alzheimer's disease diagnosis.…”

Section: Related Workmentioning

confidence: 99%

A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data

El-Assy,

Amer,

Ibrahim

et al. 2024

Sci Rep

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Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder that requires accurate diagnosis for effective management and treatment. In this article, we propose an architecture for a convolutional neural network (CNN) that utilizes magnetic resonance imaging (MRI) data from the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset to categorize AD. The network employs two separate CNN models, each with distinct filter sizes and pooling layers, which are concatenated in a classification layer. The multi-class problem is addressed across three, four, and five categories. The proposed CNN architecture achieves exceptional accuracies of 99.43%, 99.57%, and 99.13%, respectively. These high accuracies demonstrate the efficacy of the network in capturing and discerning relevant features from MRI images, enabling precise classification of AD subtypes and stages. The network architecture leverages the hierarchical nature of convolutional layers, pooling layers, and fully connected layers to extract both local and global patterns from the data, facilitating accurate discrimination between different AD categories. Accurate classification of AD carries significant clinical implications, including early detection, personalized treatment planning, disease monitoring, and prognostic assessment. The reported accuracy underscores the potential of the proposed CNN architecture to assist medical professionals and researchers in making precise and informed judgments regarding AD patients.

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Section: Related Workmentioning

confidence: 99%

A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data

El-Assy,

Amer,

Ibrahim

et al. 2024

Sci Rep

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show abstract

“…This hierarchical feature extraction process enables CNNs to effectively analyse and understand complex visual patterns in images. As a result, CNNs have achieved significant success in various computer vision tasks [37,38].…”

Section: Artificial Neural Network Structurementioning

confidence: 99%

Prediction of Wind Power with Machine Learning Models

Karaman

2023

Applied Sciences

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Wind power is a vital power grid component, and wind power forecasting represents a challenging task. In this study, a series of multiobjective predictive models were created utilising a range of cutting-edge machine learning (ML) methodologies, namely, artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks, and long short-term memory (LSTM) networks. In this study, two independent data sets were combined and used to predict wind power. The first data set contained internal values such as wind speed (m/s), wind direction (°), theoretical power (kW), and active power (kW). The second data set was external values that contained the meteorological data set, which can affect the wind power forecast. The k-nearest neighbours (kNN) algorithm completed the missing data in the data set. The results showed that the LSTM, RNN, CNN, and ANN algorithms were powerful in forecasting wind power. Furthermore, the performance of these models was evaluated by incorporating statistical indicators of performance deviation to demonstrate the efficacy of the employed methodology effectively. Moreover, the performance of these models was evaluated by incorporating statistical indicators of performance deviation, including the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) metrics to effectively demonstrate the efficacy of the employed methodology. When the metrics are examined, it can be said that ANN, RNN, CNN, and LSTM methods effectively forecast wind power. However, it can be said that the LSTM model is more successful in estimating the wind power with an R2 value of 0.9574, MAE of 0.0209, MSE of 0.0038, and RMSE of 0.0614.

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“…DL, a subset of machine learning, is a relatively new technique developed to overcome the shortcomings of shallow learning models [38,39] DL-based methods have been successfully applied to classification [40] and prediction problems [41]. LSTM, a variant of RNN, can learn time-series information more accurately.…”

Section: Related Workmentioning

confidence: 99%

Multilayer LSTM Model for Wind Power Estimation in the Scada System

ÇELEBİ,

KARAMAN

2023

EJT

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Wind energy is clean energy that does not pollute the environment. However, the complex and variable operating environment of a wind turbine often makes it difficult to predict the instantaneous active power generated. In this study, a wind turbine active power estimation system based on a short-term memory network (LSTM) using time series analysis is proposed. The data obtained from the wind turbine SCADA system is used as input variables. In the proposed method, a multilayer LSTM architecture is designed to train the model. The first LSTM network consists of 64 units, and the second one consists of 32 units. This is followed by a dense layer consisting of 16 neurons. In the last layer, the architecture is finalized by using a linear activation function for the prediction process. The proposed deep learning (DL)-based LSTM prediction model takes into account environmental factors such as wind speed and wind direction for active power forecasting. The results show that the LSTM-based time series analysis method is capable of effectively capturing time series features among the data. Thus, the proposed architecture can realize high-accuracy active power forecasting.

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A Novel Deep Dense Block-Based Model for Detecting Alzheimer’s Disease

Cited by 5 publications

References 54 publications

A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data

A novel CNN architecture for accurate early detection and classification of Alzheimer’s disease using MRI data

Prediction of Wind Power with Machine Learning Models

Multilayer LSTM Model for Wind Power Estimation in the Scada System

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