Computer Aided Diagnosis System Feature Extraction of Alzheimer Disease Using MFCC

Dessouky, Mohamed M.; El-Rashidy, Mohamed A.; Taha, Taha E.; Abdelkader, Hatem

doi:10.1080/1931308x.2015.1004823

Cited by 5 publications

(7 citation statements)

References 12 publications

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“…This section presents the obtained results of the metric parameters used for measuring the performance of the first proposed algorithm given in [30] and the second proposed algorithm based on MFCC technique given in [31]. The obtained results depicts the variation of SEN, SPE.…”

Section: Resultsmentioning

confidence: 99%

“…This paper discusses two proposed feature extraction algorithms for 3D MRI Alzheimer's disease images [30,31]. These two proposed algorithms are discussed in next sub-sections.…”

Section: The Proposed Algorithmsmentioning

confidence: 99%

“…These stages are summarized as following [31,38] The steps 1, 2, 3, 5, and 6 are discussed previously in the first proposed.…”

Section: The Second Proposed Algorithmmentioning

confidence: 99%

“…For the first proposed algorithm and Table 4, the accuracy (ACC) reached to 100% at two techniques: Table 3: Sensitivity, Specificity, Accuracy, and MCC of the classifier for the second proposed algorithm given in [31,38]. Figure 12: Sensitivity, Specificity, Accuracy, and MCC of the classifier for the second proposed algorithm given in [31]. The specificity (SPE) reached to 100% at all five optimization techniques and the best technique is the Pattern Search Algorithm which has small number of features equal to 1 feature and small number of iterations equal to 20 iterations.…”

Section: -D Signalmentioning

confidence: 99%

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Feature Extraction of the Alzheimer’s Disease Images Using Different Optimization Algorithms

Dessouky¹,

El-Rashidy²

2016

J Alzheimers Dis Parkinsonism

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Alzheimer's disease (AD) is a type of dementia that causes problems with memory, thinking and behavior. The symptoms of the AD are usually developed slowly and got worse over time, till reach to severe enough stage which can't interfere with daily tasks. This paper extract the most significant features from 3D MRI AD images using different optimization algorithms. Optimization algorithms are stochastic search methods that simulate the social behavior of species or the natural biological evolution. These algorithms had been used to get near-optimum solutions for large-scale optimization problems. This paper compares the formulation and results of five recent evolutionary optimization algorithms: Particle Swarm Optimization, Bat Algorithm, Genetic Algorithm, Pattern Search, and Simulated Annealing. A brief description of each of these five algorithms had been presented. These five optimization algorithm had been applied to two proposed AD feature extraction algorithms to get near-optimum number of features that gives higher accuracy. The comparisons among the algorithms are presented in terms of number of iteration, number of features and metric parameters. The results show that the Pattern Search optimization algorithm gives higher metric parameters values with lower number of iteration and lower number of features as compared to the other optimization algorithms.

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Section: Resultsmentioning

confidence: 99%

“…This paper discusses two proposed feature extraction algorithms for 3D MRI Alzheimer's disease images [30,31]. These two proposed algorithms are discussed in next sub-sections.…”

Section: The Proposed Algorithmsmentioning

confidence: 99%

“…These stages are summarized as following [31,38] The steps 1, 2, 3, 5, and 6 are discussed previously in the first proposed.…”

Section: The Second Proposed Algorithmmentioning

confidence: 99%

Section: -D Signalmentioning

confidence: 99%

See 2 more Smart Citations

Feature Extraction of the Alzheimer’s Disease Images Using Different Optimization Algorithms

Dessouky¹,

El-Rashidy²

2016

J Alzheimers Dis Parkinsonism

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“…Each classifier is tested using 30 samples (containing both seizures and normal samples). To test the results, the true positive, the true negative, the false positive and the false negative are defined as [ 47 ]: True positive ( TP ): positive (patient) samples correctly classified as positive (patient) samples. False positive ( FP ): negative (normal) samples incorrectly classified as positive (patient) samples.…”

Section: Proposed Approachmentioning

confidence: 99%

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

et al. 2021

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Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure using several methods such as neurological examination, blood tests, computerized tomography (CT), magnetic resonance imaging (MRI) and electroencephalogram (EEG). Medical data, such as the EEG signal, usually includes a number of features and attributes that do not contains important information. This paper proposes an automatic seizure classification system based on extracting the most significant EEG features for seizure diagnosis. The proposed algorithm consists of five steps. The first step is the channel selection to minimize dimensionality by selecting the most affected channels using the variance parameter. The second step is the feature extraction to extract the most relevant features, 11 features, from the selected channels. The third step is to average the 11 features extracted from each channel. Next, the fourth step is the classification of the average features using the classification step. Finally, cross-validation and testing the proposed algorithm by dividing the dataset into training and testing sets. This paper presents a comparative study of seven classifiers. These classifiers were tested using two different methods: random case testing and continuous case testing. In the random case process, the KNN classifier had greater precision, specificity, positive predictability than the other classifiers. Still, the ensemble classifier had a higher sensitivity and a lower miss-rate (2.3%) than the other classifiers. For the continuous case test method, the ensemble classifier had higher metric parameters than the other classifiers. In addition, the ensemble classifier was able to detect all seizure cases without any mistake.

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Intelligent Computing Application for Cloud Enhancing Healthcare Services

Haldorai¹,

Ramu

2021

Computer Networks, Big Data and IoT

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Computer Aided Diagnosis System Feature Extraction of Alzheimer Disease Using MFCC

Cited by 5 publications

References 12 publications

Feature Extraction of the Alzheimer’s Disease Images Using Different Optimization Algorithms

Feature Extraction of the Alzheimer’s Disease Images Using Different Optimization Algorithms

Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals

Intelligent Computing Application for Cloud Enhancing Healthcare Services

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