IDAP 2016

A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses

Bu yıl İnönü Üniversitesi Bilgisayar Mühendisliği Bölümü "International Artificial Intelligence and Data Processing Symposium'16" adı altında bir konferans düzenledi. "A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses" başklı bir çalışma ile konferansa katılım sağladık. Sunduğumuz çalışmaya Research Gate üzerinden erişebilirsiniz.

Yapay zeka ve uygulamaları, makine öğrenmesi, derin öğrenme, örüntü ve nesne tanıma, biyomedikal işaret ve görüntü işleme gibi pek çok konuya dair bilimsel çalışmaların sunulduğu konferansın geleneksel olarak her Eylül ayının ilk haftasonu düzenlenmesi planlanıyor. Etkinliğin zamanla çok büyüyeceğini ve çok etkili olacağını düşünüyorum. 

A Study Based on Gray Level Co-Occurrence Matrix and Neural Network Community for Determination of Hypoxic Fetuses

Abstract—Cardiotocography (CTG) is a monitoring technique used routinely during the pregnancy and labor and including the analysis of fetal heart rates and movements with uterine contractions. The fact that CTG signals are interpreted by experts generally with eye and CTG has high false positive rate results in intra- and inter-observer conflicts and causes the observers to frequently notice real pathological cases. Therefore, various computer-aided methods supporting diagnosis process have been developed. In this study, a new approach is suggested based on signal and image processing techniques in order to provide classification of CTG signals. In particular, morphological, spectral and statistical properties of CTG signals are obtained with the way defined conventionally. A spectrum of the signals containing time-frequency information was transformed into 8-bit gray-level image and it was enabled to build grey-level co-occurrence matrix (GLCM). In the final step, a combination of morphological, statistical, spectral and image-based properties was applied as input to the artificial neural network (ANN). In order to measure the performance of the proposed method, accuracy, sensitivity, specificity and quality indexes were used. The obtained results revealed that image-based features increased the classification success and they gave the best results when they were used with the conventional features.  

Keywords— Cardiotocography, Fetal Heart Rate, GLCM, Classification, Artificial Neural Networks

İndir - BEU - PBS 

2016-09-24, Cumartesi
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