Plantar Basınç Dağılımı Sinyalleri Kullanılarak Erken MSlilerde Ataksinin Hybrt CNN Modelleri ile Belirlenmesi

Year 2021, Issue: 28, 579 - 583, 30.11.2021

Aslı Sesli , Seda Arslan Tuncer , Furkan Bilek

https://doi.org/10.31590/ejosat.1009129

Cited By: 1

Abstract

Multipl Skleroz (MS), ataksi ve denge bozukluklarına neden olan bir merkezi sinir sistemi hastalığıdır. Atakside genellikle ilk semptom yürüyüş bozukluğu olarak görülmektedir. Yürüyüş ataksisi klinik olarak artmış çift destek süresi, kısalmış adım uzunluğu ve düzensiz adımlar ile tanımlanabilir. Bu yüzden ataksi tespitinde yürüme bozukluğunun değerlendirilmesi doğru bir yol olacaktır. Derin öğrenme çok sayıda girdi verisinden özellik çıkararak çıktı verisini tahmin eden bir makine öğrenmesi yöntemidir. Derin öğrenme nesne tanıma, sınıflandırma ve sinyal işleme gibi alanlarda sıklıkla kullanılmaktadır. Bu çalışmada plantar basınç dağılım sinyalleri içeren görüntüler kullanılarak MS’li bireyler (PwMS) için ataksi tespiti yapılması amaçlanmıştır. Bu amaçla PwMSi olan ve sağlıklı olan bireylerin plantar basınç dağılım sinyallerini içeren toplam 418 görüntü önceden eğitilmiş Hybrit CNN ağlar yardımıyla sınıflandırılmıştır. Veri setinden özellik çıkarılırken VGG16, VGG19, ResNet, MobilNet ve NasNEt derin öğrenme mimarileri kullanıldı. Daha sonra elde edilen özellik vektörleri SVM, KNN ve ANN sınıflandırıcıları kullanılarak sınıflandırıldı. Bu çalışma sonucunda en iyi sınıflandırma performansı,SVM sınıflandırıcısı ile VGG19 %85.71 Acc %81.81 Sen, %88.23 Spe derin öğrenme mimarisi kullanılarak elde edilmiştir. Yapılan bu çalışmanın yapay zeka yardımı ile PwMS’de ataksi tespitinde hekime yardımcı olacağı kanaatine varılmıştır.

Keywords

Multiple Skleroz, Ataksi, Derin Öğrenme, Sınıflandırma

Determination of Ataxia With Hybrt CNN Models in Early MS Using Plantar Pressure Distribution Signals

Abstract

Multiple sclerosis (MS) is a disease of the central nervous system that causes ataxia and deficits in balance.In ataxia, the first symptom is usually seen as gait disturbance. Gait ataxia can be clinically defined by increased double support time, shortened stride length, and irregular strides. In this direction, the evaluation of deterioration in the detection of ataxia would be the right way. Deep learning is a machine learning method that predicts output data by extracting features from a large number of input data. Deep learning is frequently used in areas such as object recognition, classification and signal processing. In this study, it was aimed to detect ataxia for individuals with MS (PwMS) using images containing plantar pressure distribution signals. For this purpose, a total of 418 images containing the plantar pressure distribution signals of healthy individuals with PwMSi were classified with the help of pre-trained Hybrid CNN networks. VGG16, VGG19, ResNet, MobilNet and NasNEt deep learning architectures were used to extract features from the dataset. Then the obtained feature vectors were classified using SVM, KNN and ANN classifiers. As a result of this study, the best classification performance was obtained by using the SVM classifier and VGG19 85.71% Acc 81.81% Sen, 88.23% Spe deep learning architecture. It was concluded that this study will help the physician in the detection of ataxia in PwMS with the help of artificial intelligence.

Keywords

Multiple Sclerosis, Ataxia, Deep Learning, Classification

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