RETRACTED ARTICLE: Automated speech based evaluation of mild cognitive impairment and Alzheimer’s disease detection using with deep belief network model

AI-Atroshi, Chiai; Beulah, J. Rene; Singamaneni, Kranthi Kumar; Cyril, C. Pretty Diana; Neelakandan, S.; Velmurugan, S.

doi:10.1080/20479700.2022.2097764

Cited by 24 publications

(11 citation statements)

References 22 publications

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“…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. Additionally, AI-Atroshi et al ( 2022 ) utilized convolutional layers with freeze elements from ImageNet, achieving 99.27% accuracy on ADNI's MRI data collection for both binary and ternary classification. Authors in Shankar et al ( 2022 ) employed a ResNet-18 architecture using a transfer learning concept and obtained an accuracy of 83.3% on Kaggle datasets.…”

Section: Results and Analysismentioning

confidence: 99%

“…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%. Shankar et al ( 2022 ) used HAAR-based object identification techniques because they are more suitable with discriminant attributes and generated 37 spatial pieces of information from seven characteristics that produced 94.1% accuracy on the dataset taken from ADNI.…”

Section: Related Workmentioning

confidence: 99%

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

Yaqoob,

Khan,

Masood

et al. 2024

Front. Comput. Neurosci.

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Alzheimer's disease (AD) is a neurodegenerative illness that impairs cognition, function, and behavior by causing irreversible damage to multiple brain areas, including the hippocampus. The suffering of the patients and their family members will be lessened with an early diagnosis of AD. The automatic diagnosis technique is widely required due to the shortage of medical experts and eases the burden of medical staff. The automatic artificial intelligence (AI)-based computerized method can help experts achieve better diagnosis accuracy and precision rates. This study proposes a new automated framework for AD stage prediction based on the ResNet-Self architecture and Fuzzy Entropy-controlled Path-Finding Algorithm (FEcPFA). A data augmentation technique has been utilized to resolve the dataset imbalance issue. In the next step, we proposed a new deep-learning model based on the self-attention module. A ResNet-50 architecture is modified and connected with a self-attention block for important information extraction. The hyperparameters were optimized using Bayesian optimization (BO) and then utilized to train the model, which was subsequently employed for feature extraction. The self-attention extracted features were optimized using the proposed FEcPFA. The best features were selected using FEcPFA and passed to the machine learning classifiers for the final classification. The experimental process utilized a publicly available MRI dataset and achieved an improved accuracy of 99.9%. The results were compared with state-of-the-art (SOTA) techniques, demonstrating the improvement of the proposed framework in terms of accuracy and time efficiency.

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Section: Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Yaqoob,

Khan,

Masood

et al. 2024

Front. Comput. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Figure 4 depicts the global sensitivity, specificity, and precision analysis of the FSHDL-HDDC method after 10 iterations. e results revealed that the FSHDL-HDDC algorithm had reached improved outcomes under every iteration [28][29][30][31][32]. For instance, with iteration-1, the FSHDL-HDDC method has offered sens y , spec y , and prec n of 98.17%, 97.12%, and 97.58%, respectively.…”

Section: Experimental Validationmentioning

confidence: 97%

A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the e-Healthcare Environment

Latha

Annamalai

Kallimani³

et al. 2022

Computational Intelligence and Neuroscience

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With the advancements in data mining, wearables, and cloud computing, online disease diagnosis services have been widely employed in the e-healthcare environment and improved the quality of the services. The e-healthcare services help to reduce the death rate by the earlier identification of the diseases. Simultaneously, heart disease (HD) is a deadly disorder, and patient survival depends on early diagnosis of HD. Early HD diagnosis and categorization play a key role in the analysis of clinical data. In the context of e-healthcare, we provide a novel feature selection with hybrid deep learning-based heart disease detection and classification (FSHDL-HDDC) model. The two primary preprocessing processes of the FSHDL-HDDC approach are data normalisation and the replacement of missing values. The FSHDL-HDDC method also necessitates the development of a feature selection method based on the elite opposition-based squirrel searchalgorithm (EO-SSA) in order to determine the optimal subset of features. Moreover, an attention-based convolutional neural network (ACNN) with long short-term memory (LSTM), called (ACNN-LSTM) model, is utilized for the detection of HD by using medical data. An extensive experimental study is performed to ensure the improved classification performance of the FSHDL-HDDC technique. A detailed comparison study reported the betterment of the FSHDL-HDDC method on existing techniques interms of different performance measures. The suggested system, the FSHDL-HDDC, has reached its maximum level of accuracy, which is 0.9772.

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“…• When determining which species of chicken to include in a group, the chicken's own fitness values are considered. 31 There are numerous roosters in the entire flock, and the rest of the chickens are categorized as chicks or hens, depending on their fitness levels. Randomly, the hen and chick create a mother-child bond and belong to the same subgroup.…”

Section: Chicken Swarm Algorithmmentioning

confidence: 99%

Deep learning modified neural networks with chicken swarm optimization-based lungs disease detection and severity classification

et al. 2023

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Many people around the world are affected by pulmonary disease as well as asthma, pneumonia, lung cancer, and tuberculosis. All of these conditions have one thing in common: airway obstruction. Lung illnesses are a worldwide issue in which chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and fibrosis are few of the most common. An accurate diagnosis of a lung problem is essential, and it has been pursued by many researchers using image processing and machine learning models. Multiple deep learning methods are used to predict lung disease; these include convolutional neural networks, vanilla neural networks, visual geometry group-based neural networks, and the capsule network. Medical data are scarce, so diagnosing pulmonary diseases using chest x-ray pictures from datasets with less than 1000 samples is considered to address the problem. Three deep learning multiple neural networks (DLMNNs) were generated using the chicken swarm optimization (CSO) approach, for which transfer learning was applied to evaluate the performance of each. First, we created an algorithm for segmenting CXR images, and then we compared it with other classification systems. Our results were compared with those of other methods using publicly available data from the Shenzhen and Montgomery lung datasets. However, our technique has a lower number of trainable parameters compared with the best-performing models trained on the Montgomery dataset. The DLMNN-CSO virtually matched the best performance on the Shenzhen dataset, although it was computationally less expensive than the other models. DLMNN-CSO's validation loss was 0.4, whereas the validation loss for CNN, VDSNet, VGG, and DBN was 0.9, 0.8, 0.6, and 0.5, respectively.

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RETRACTED ARTICLE: Automated speech based evaluation of mild cognitive impairment and Alzheimer’s disease detection using with deep belief network model

Cited by 24 publications

References 22 publications

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

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

A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the e-Healthcare Environment

Deep learning modified neural networks with chicken swarm optimization-based lungs disease detection and severity classification

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