A Machine Learning Based Fall Detection for Elderly People with Neurodegenerative Disorders

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Alzheimer's disease, an incurable chronic neurological disorder (NLD) that affects human memory and demises cognitive thinking ability with shrinkage of the brain area. Early detection of Alzheimer's disease (AD) is the only hope to delay its effect. This study designed a computer-aided automated detection method that can detect mild cognitive impairment for AD from magnetic resonance image scans. The datadriven solution approach requires an extensive quantity of annotated images for diagnosis. However, obtaining a large amount of annotated data for medical application is a challenging task. We have exploited a deep convolutional generative adversarial network (DCGAN) for synthesizing high-quality images to increase dataset size. A fine-tuned CNN (VGG16 architecture) model works on images to extract the intuitive features for early diagnosis. The extracted features of images by VGG16 feed into the support vector machine for classification. This research has conducted copious experiments to validate the proposed method outperformed relative baselines on public datasets.

Section: Resultsmentioning

confidence: 99%

D3mciAD: Data-Driven Diagnosis of Mild Cognitive Impairment Utilizing Syntactic Images Generation and Neural Nets

Hasan

Asaduzzaman

Rahman

et al. 2021

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“…The data of our Parkinson disease voice dataset is limited. In future we will collect more data to detect Parkinson disease and we will work with some deep learning method and other method [2][3][4]7,8,14,17,18,20,27,28,33,38]. In future, we will also work with Parkinson disease MRI Data.…”

Section: Discussionmentioning

confidence: 99%

Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease

Nahar

Ara

Neloy

et al. 2021

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Parkinson's disease (PD) is a kind of neurodegenerative disorder characterized by the loss of dopamine-producing cells in the brain. The disruption of brain cells that create dopamine, a chemical that allows brain cells to connect with one another, causes Parkinson's disease. Control, adaptability, and rapidity of movement are all controlled by dopamine-producing cells in the brain. Researchers have been investigating for techniques to identify non-motor symptoms that show early in the disease as soon as possible, slowing the disease's progression. A machine learning-based detection of Parkinson's disease is proposed in this research. Feature selection and classification techniques are used in the proposed detection technique. Boruta, Recursive Feature Elimination (RFE) and Random Forest (RF) Classifier have been used for the feature selection process. Four classification algorithms are considered to detect Parkinson disease which are gradient boosting, extreme gradient boosting, bagging and Extra Tree Classifier. Bagging with recursive feature elimination was found to outperform the other methods. The lowest number of voice characteristics for the diagnosis in Parkinson attained 82.35% accuracy.

“…Further, this model can be enhanced to classify other syndromes like PD and we use other model to predict PD[?] [2,[4][5][6]8,9,14,15,20,22,29].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Approach to Classify Parkinson’s Disease from MRI Samples

Basnin

Nahar

Anika

et al. 2021

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Perkinson's disease is a progressive degenerative disorder that comes from a recognized clinical parkinsonian syndrome. The manifestations of Parkinson's disease include both motor and nonmotor symptoms identified as tremor, bradykinesia (slowed movements), rigidity, and postural instability. PD is marked as one of the most prevalent disorders from various researches and surveys because it has been observed in 90% of people out of 100. It is imperative to design CAD to develop an advanced model for the determination of this disease with accuracy since up to date there is no accurate clinical intervention for the diagnosis of PD. In contrast to conventional methods. Deep learning convolutional neural network tools are implied for the faster and accurate identification of PD through MRI. The purpose of this research is to contribute to the development of an accurate PD detection method. To conduct the research a public dataset NTU (National Technical University of Athens) is used. The data samples are categorized into three sets (Training, Test, and Validation). A DenseNet integrated with LSTM is applied to the MRI data samples. DenseNet is used to strengthen the feature selection ability, as each layer selects features depending on the temporal closeness of the image. The output is then fed into the LSTM layer, for discovering the significant dependencies in temporal features. The performance of the proposed DenseNet-LSTM is compared to other CNN state-of-theart models. The proposed model outputs a training accuracy of 93.75%, testing accuracy of 90%, and validation accuracy of 93.8% respectively.