The hybrid feature extraction method for classification of adolescence idiopathic scoliosis using Evolving Spiking Neural Network

Sabri, Nurbaity; Hamed, Haza Nuzly Abdull; İbrahim, Zarina Bibi; Ibrahim, Kamalnizat; Isa, Mohd Adham; Diah, Norizan Mat

doi:10.1016/j.jksuci.2022.08.019

Cited by 4 publications

(1 citation statement)

References 57 publications

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“…To improve the classification performance, optimal feature extraction methods are developed. The feature extraction is considered as a form of dimensionality reduction [13]. The process of getting the most active properties from the original input while concurrently minimizing the variability within a class and maximizing the variability across classes can be referred to as feature extraction or feature selection [14].…”

Section: Introductionmentioning

confidence: 99%

A Hybridized Feature Extraction Model for Offline Yorùbá Document Recognition

Ajao

Isiaka

Babatunde

2023

AJRCoS

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Document recognition is required to convert handwritten and text documents into digital equivalents, making them more easily accessible and convenient to store. This study combined feature extraction techniques for recognizing Yorùbá documents in an effort to preserve the cultural values and heritages of the Yorùbá people. Ten Yorùbá documents were acquired from Kwara State University’s Library, and ten indigenous literate writers wrote the handwritten version of the documents. These were digitized using HP Scanjet300 and pre-processed. The pre-processed image served as input to the Local Binary Pattern, Speeded-Up-Robust-Features and Histogram of Gradient. The combined extracted feature vectors were input into the Genetic Algorithm. The reduced feature vector was fed into Support Vector Machine. A 10-folds cross-validation was used to train the model: LBP-GA, SURF-GA, HOG-GA, LBP-SURF-GA, HOG-SURF-GA, LBP-HOG-GA and LBP-HOG-SURF-GA. LBP-HOG-SURF-GA for Yorùbá printed text gave 90.0% precision, 90.3% accuracy and 15.5% FPR. LBP-HOG-SURF-GA for Handwritten Yorùbá document showed 80.9% precision, 82.6% accuracy and 20.4% (FPR) LBP-HOG-SURF-GA for CEDAR gave 98.0% precision, 98.4% accuracy and 2.6% FPR. LBP-HOG-SURF-GA for MNIST gave 99% precision, 99.5% accuracy, 99.0% and 1.1% FPR. The results of the hybridized feature extractions (LBP-HOG-SURF) demonstrated that the proposed work improves significantly on the various classification metrics.

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Section: Introductionmentioning

confidence: 99%

A Hybridized Feature Extraction Model for Offline Yorùbá Document Recognition

Ajao

Isiaka

Babatunde

2023

AJRCoS

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Non-invasive Scoliosis Assessment in Adolescents

Cheng,

Lu,

Sun

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

Goral

Köse

2022

IEEE Access

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Scoliosis is a disease caused by the spine curving. It is treatable but physiotherapists may do different measurements for curvature angles. That's a problem affecting the treatment planning. This study aims to develop a Deep Learning-based decision support system, which diagnoses scoliosis and plans treatments via Schroth method. The system has an interpretable and explainable CapsNet model processing x-ray image to detect 68-point vertebrae and make Cobb angle measurements. By using angle values and patient parameters, treatment is planned through an automated Schroth definition and the Fuzzy Logic. In the evaluations, the CapsNet had dominating findings (some of them are MSE: 0.0038, PCC: 0.93, Accuracy: 0.98). The Fuzzy Logic model was accurate at exercise plans for past cases. Also, physiotherapists and patients had positive feedback for the system usage, trustworthiness, diagnosis, treatment planning and tracking. As a conclusion, the system ensures advancements for automated diagnosis and treatment of scoliosis.

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The hybrid feature extraction method for classification of adolescence idiopathic scoliosis using Evolving Spiking Neural Network

Cited by 4 publications

References 57 publications

A Hybridized Feature Extraction Model for Offline Yorùbá Document Recognition

A Hybridized Feature Extraction Model for Offline Yorùbá Document Recognition

Non-invasive Scoliosis Assessment in Adolescents

Development of a CapsNet and Fuzzy Logic Decision Support System for Diagnosing the Scoliosis and Planning Treatments via Schroth Method

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