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Integrated Evolving Spiking Neural Network and Feature Extraction Methods for Scoliosis Classification

Computers, Materials & Continua 2022年第12期73卷 5559-5573页

作者： Nurbaity Sabri Haza Nuzly Abdull Hamed Zaidah ibrahim kamalnizat ibrahim Mohd Adham Isa Faculty of Engineering Universiti Teknologi MalaysiaSchool of ComputingSkudai81310JohorMalaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Cawangan Melaka Kampus JasinMerlimau77300MelakaMalaysia Faculty of Computer and Mathematical Sciences University Teknologi MARAShah Alam40450SelangorMalaysia Fakulti Perubatan&Kesihatan Pertahanan Universiti Pertahanan Nasional Malaysia(UPNM)Sungai Besi57000Kuala LumpurMalaysia

Adolescent Idiopathic Scoliosis(AIS)is a deformity of the spine that affects *** current method for detecting AIS is based on radiographic images which may increase the risk of cancer growth due to *** is another alternative used to identify AIS by distinguishing the curves of the spine from the surface of a human’s ***,detecting the curve of the spine is manually performed,making it a time-consuming *** overcome this issue,it is crucial to develop a better model that automatically detects the curve of the spine and classify the types of *** research proposes a new integration of ESNN and Feature Extraction(FE)methods and explores the architecture of ESNN for the AIS classification *** research identifies the optimal Feature Extraction(FE)methods to reduce computational *** ability of ESNN to provide a fast result with a simplicity and performance capability makes this model suitable to be implemented in a clinical setting where a quick result is crucial.A comparison between the conventional classifier(Support Vector Machine(SVM),Multi-layer Perceptron(MLP)and Random Forest(RF))with the proposed AIS model also be performed on a dataset collected by an orthopedic expert from Hospital Universiti Kebangsaan Malaysia(HUKM).This dataset consists of various photogrammetry images of the human back with different types ofMalaysian AIS patients to solve the scoliosis *** process begins by pre-processing the images which includes resizing and converting the captured pictures to gray-scale *** is then followed by feature extraction,normalization,and *** experimental results indicate that the integration of LBP and ESNN achieves higher accuracy compared to the performance of multiple baseline state-of-the-art Machine Learning for AIS *** demonstrates the capability of ESNN in classifying the types of AIS based on photogrammetry images.

关键词： Adolescent idiopathic scoliosis evolving spiking neural network lenke type local binary pattern photogrammetry

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