Comparison of Artificial Neural Network and Extreme Learning Machine in Benign Liver Lesions Classification
| dc.contributor.author | Akin, Mustafa | |
| dc.contributor.author | Ceylan, Murat | |
| dc.date.accessioned | 2020-03-26T19:01:28Z | |
| dc.date.available | 2020-03-26T19:01:28Z | |
| dc.date.issued | 2015 | |
| dc.department | Selçuk Üniversitesi | en_US |
| dc.description | Medical Technologies National Conference (TIPTEKNO) -- OCT 15-18, 2015 -- Bodrum, TURKEY | en_US |
| dc.description.abstract | 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. | en_US |
| dc.identifier.isbn | 978-1-4673-7765-2 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12395/31948 | |
| dc.identifier.wos | WOS:000380505200058 | en_US |
| dc.identifier.wosquality | N/A | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.language.iso | tr | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.ispartof | 2015 MEDICAL TECHNOLOGIES NATIONAL CONFERENCE (TIPTEKNO) | en_US |
| dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.selcuk | 20240510_oaig | en_US |
| dc.subject | wavelet transform | en_US |
| dc.subject | complex-valued wavelet transform | en_US |
| dc.subject | artificial neural network | en_US |
| dc.subject | extreme learning machine | en_US |
| dc.subject | cyst | en_US |
| dc.subject | hemangioma | en_US |
| dc.subject | liver classification | en_US |
| dc.title | Comparison of Artificial Neural Network and Extreme Learning Machine in Benign Liver Lesions Classification | en_US |
| dc.type | Conference Object | en_US |
