Comparison of artificial neural network and extreme learning machine in benign liver lesions classification
Küçük Resim Yok
Tarih
2016
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, the classification of the most common benign lesions, cysts and hemangiomas in liver was achieved using magnetic resonance (MR) images. T1 venous phase of 68 liver MR images were used for the classification, including 28 cysts and 40 hemangiomas MR images. Liver segmentation was done by expert radiologists using MR images. Then automatic windowing was applied to images to reduce the negative impact on the process of image-free areas of tissue information. The obtained images were normalized and thresholded using histogram equalization. The average, standard deviation and distortion values of the image feature matrix obtained by applying wavelet transform (WT) and complex valued wavelet transform (CVWT) onto the thresholded images were calculated. Artificial neural network (ANN) ,extreme learning machine (ELM), cyst and hemangiomas classification were achieved using these features as inputs. As a result of this study,50% accuracy at the data applied CVWT, 70,5% accuracy at the data applied WT were obtained in ANN. Average processing time is 4.61 seconds. When examined the ELM application results, it can be seen that there are 55, 8% accuracy at the data applied CVWT and 62, 5% accuracy at the data applied WT. Also, the average processing time is 0,016 seconds this time. Although the classification results seem low, classification accuracy rates will increase with the development studies considering advantage of ELM processing time. © 2015 IEEE.
Açıklama
Medical Technologies National Conference, TIPTEKNO 2015 -- 15 October 2015 through 18 October 2015 -- 118954
Anahtar Kelimeler
artificial neural network, complex-valued wavelet transform, cyst, extreme learning machine, hemangioma, liver classification, wavelet transform
Kaynak
2015 Medical Technologies National Conference, TIPTEKNO 2015
WoS Q Değeri
Scopus Q Değeri
N/A
