Gait Recognition in the Classification of Neurodegenerative Diseases

Sánchez-DelaCruz, Eddy; Acosta, Francisco; Wister, Miguel A.; Hernández-Nolasco, José Adán; Pancardo, Pablo; Méndez-Castillo, Juan José

doi:10.1007/978-3-319-13102-3_23

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…In this research, we propose to identify the phases of cervical cancer using assembled algorithms to minimize error margin, because with this approach has been reached competitive percentages in breast cancer detection [5] and categorization of neurodegenerative diseases [15,14] . In section four it will be discuss more in detail of the methodology that is followed to solve the problem.…”

Section: Proposed Solutionmentioning

confidence: 99%

Classification of Cervical Cancer Using Assembled Algorithms in Microscopic Images of Papanicolaou

Gómez¹,

Sánchez-DelaCruz²,

Mata³

2017

RCS

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In Mexico cervical cancer is the second leading cause of death from malignant neoplasms in women, but this mortality rate has been reduced in recent years thanks to early detection programs as the pap smear test, which is aimed at finding pre-cancerous abnormalities in cells that cover the cervix. The pap smear test is an efficient medical test, but it presents problems at the moment of interpretation under the microscope, due to the large number of cells in the sample and others external factors. In order to solve this disadvantage, computational techniques are used to support the samples classification. In this research we propose to use assembled algorithms to construct a classifier. The database used is from Herlev University Hospital, the data were formulated as a binary classification problem. The results of the experiments (exhaustive search) show that using the combinations of algorithms Bagging+MultilayerPerceptron and AdaBoostM1+LMT is obtained a high percentage of correctly classified instances, 95.74%.

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Section: Proposed Solutionmentioning

confidence: 99%

Classification of Cervical Cancer Using Assembled Algorithms in Microscopic Images of Papanicolaou

Gómez¹,

Sánchez-DelaCruz²,

Mata³

2017

RCS

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“…There are many approaches being successfully applied in diagnosing NDs which include the kernel Fisher discriminant (KFD), the naive Bayesian approach (NB), support vector machine (SVM) and nearest neighbor (NN) [3,[14][15][16]. In addition, neural networks have also been adopted in this field [4,17].…”

Section: Introductionmentioning

confidence: 99%

LSTM for diagnosis of neurodegenerative diseases using gait data

Zhao

et al. 2018

Ninth International Conference on Graphic and Image Processing (ICGIP 2017)

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Neurodegenerative diseases (NDs) usually cause gait disorders and postural disorders, which provides an important basis for NDs diagnosis. By observing and analyzing these clinical manifestations, medical specialists finally give diagnostic results to the patient, which is inefficient and can be easily affected by doctors' subjectivity. In this paper, we propose a two-layer Long Short-Term Memory (LSTM) model to learn the gait patterns exhibited in the three NDs. The model was trained and tested using temporal data that was recorded by force-sensitive resistors including time series, such as stride interval and swing interval. Our proposed method outperforms other methods in literature in accordance with accuracy of the predicted diagnostic result. Our approach aims at providing the quantitative assessment so that to indicate the diagnosis and treatment of these neurodegenerative diseases in clinic.

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“…Single-modal Methods ALS vs. CO PD vs. CO HD vs. CO NDDs vs. CO RBF+DL [10] 89.66% 87.10% 83.33% -QBC [19] 100% 80.00% 71.43% -Meta-classifiers [17] 96.13% 90.36% 88.67% -HMM [18] -90.32% --C-FuzzyEn+SVM [21] -96.77% --PE+SVM [22] 92.86% ---Multi-modal Methods ALS vs. CO PD vs. CO HD vs. CO NDDs vs. CO DCLSTM [24] 97 Single-modal Methods Multi-modal Methods QBC [19] DCLSTM [24] The proposed method 86.96% 95.67% 98.88% of the hidden states as N = 100 with 500 training iterations.…”

Section: Methodsmentioning

confidence: 99%