An Integrated CNN-LSTM Model for Micro Hand Gesture Recognition

Basnin, Nanziba; Nahar, Lutfun; Hossain, Mohammad Shahadat

doi:10.1007/978-3-030-68154-8_35

Cited by 22 publications

(8 citation statements)

References 25 publications

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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

Lecture Notes in Computer Science

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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.

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Section: Discussionmentioning

confidence: 99%

Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease

Nahar

Ara

Neloy

et al. 2021

Lecture Notes in Computer Science

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“…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%

“…Figures 3,4,5, and,6 graphically represents the performance curve of all the state of the art model aplied to train the MRI samples for the classification of PD (Fig.7).…”

mentioning

confidence: 99%

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

Basnin

Nahar

Anika

et al. 2021

Lecture Notes in Computer Science

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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.

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“…Inception V3 [31] is a CNN pre trained model [9]. It is computationally more efficient and focuses on using less computational power.…”

Section: Inception V3mentioning

confidence: 99%

Glaucoma Detection Using Inception Convolutional Neural Network V3

Afroze

Akther

Chowdhury

et al. 2021

Communications in Computer and Information Science

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Glaucoma detection is an important research area in intelligent system and it plays an important role to medical field. Glaucoma can give rise to an irreversible blindness due to lack of proper diagnosis. Doctors need to perform many tests to diagnosis this threatening disease. It requires a lot of time and expense. Sometime affected people may not have any vision loss, at the early stage of glaucoma. For detecting glaucoma, we have built a model to lessen the time and cost. Our work introduces a CNN based Inception V3 model. We used total 6072 images. Among this image 2336 were glaucomatous and 3736 were normal fundus image. For training our model we took 5460 images and for testing we took 612 images. After that we obtained an accuracy of 0.8529 and a value of 0.9387 for AUC. For comparison, we used DenseNet121 and ResNet50 algorithm and got an accuracy of 0.8153 and 0.7761 respectively.

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An Integrated CNN-LSTM Model for Micro Hand Gesture Recognition

Cited by 22 publications

References 25 publications

Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease

Feature Selection Based Machine Learning to Improve Prediction of Parkinson Disease

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

Glaucoma Detection Using Inception Convolutional Neural Network V3

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