Investigation of bone age assessment with convolutional neural network by using DoG filtering and à trous wavelet as preprocessing techniques
Yazarlar (1)
Öğr. Gör. Fatih Zahid GENÇ Beykoz Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1109/CEIT.2018.8751885 | ||
| Kongre Adı | 2018 6th International Conference on Control Engineering Information Technology (CEIT) | ||
| Kongre Tarihi | 25-10-2018 / 27-10-2018 | ||
| Basıldığı Ülke | Basıldığı Şehir | Istanbul, Turkey | |
| Bildiri Linki | https://ieeexplore.ieee.org/document/8751885/ | ||
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Görüntü İşleme
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| Özet |
| Bone age assessment is one of the most important problems to assess Pediatric growth. Various methods have been investigated to develop bone age assessment. The study of bone age assessment helps to diagnose disorders in growth progress. It is very common to monitor growth progress with left hand x-ray images. There are many method such as Tanner-Whitehouse [TW], Greulich and Pyle (G&P) methods. In this paper, we applied convolutional neural network resnet50 by preprocessing the images using different filters based on edge detection to compare effects on CNN accuracy. The main purpose of the paper is to compare the effect of DoG filtering and à trous wavelet as preprocessing techniques on bone-age assessment. It has been proved that suggested methods improved the accuracy of CNN (resnet50) in filtered images compared to the result of the non-filtered ones. The ages between 0 and 7 are … |
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| Google Scholar | 2 |