Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/4925
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Solak, Ahmet | - |
dc.contributor.author | Ceylan, Rahime | - |
dc.contributor.author | Bozkurt, Mustafa Alper | - |
dc.contributor.author | Cebeci, Hakan | - |
dc.contributor.author | Koplay, Mustafa | - |
dc.date.accessioned | 2023-12-26T07:52:32Z | - |
dc.date.available | 2023-12-26T07:52:32Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 2196-8888 | - |
dc.identifier.issn | 2196-8896 | - |
dc.identifier.uri | https://doi.org/10.1142/S2196888823500161 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/4925 | - |
dc.description.abstract | Adrenal lesions refer to abnormalities or growths that occur in the adrenal glands, which are located on top of each kidney. These lesions can be benign or malignant and can affect the function of the adrenal glands. This paper presents a study on adrenal tumor segmentation using a modified U-Net model with various parameter selection strategies. The study investigates the effect of fine-tuning parameters, including k-fold values and batch sizes, on segmentation performance. Additionally, the study evaluates the effectiveness of different preprocessing techniques, such as Discrete Wavelet Transform (DWT), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Image Fusion, in enhancing segmentation accuracy. The results show that the proposed model outperforms the original U-Net model, achieving the highest scores for Dice, Jaccard, sensitivity, and specificity scores of 0.631, 0.533, 0.579, and 0.998, respectively, on the T1-weighted dataset with DWT applied. These results highlight the importance of parameter selection and preprocessing techniques in improving the accuracy of adrenal tumor segmentation using deep learning. | en_US |
dc.language.iso | en | en_US |
dc.publisher | World Scientific Publ Co Pte Ltd | en_US |
dc.relation.ispartof | Vietnam Journal of Computer Science | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Adrenal tumor | en_US |
dc.subject | segmentation | en_US |
dc.subject | U-Net | en_US |
dc.subject | parameter analysis | en_US |
dc.subject | deep learning | en_US |
dc.subject | System | en_US] |
dc.title | Adrenal Tumor Segmentation on U-Net: A Study About Effect of Different Parameters in Deep Learning | en_US |
dc.type | Article | en_US |
dc.type | Article; Early Access | en_US |
dc.identifier.doi | 10.1142/S2196888823500161 | - |
dc.identifier.scopus | 2-s2.0-85177083598 | en_US |
dc.department | KTÜN | en_US |
dc.identifier.wos | WOS:001104639000001 | en_US |
dc.institutionauthor | … | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57221815832 | - |
dc.authorscopusid | 12244684600 | - |
dc.authorscopusid | 57407810400 | - |
dc.authorscopusid | 56033553000 | - |
dc.authorscopusid | 55920818900 | - |
item.fulltext | No Fulltext | - |
item.openairetype | Article | - |
item.openairetype | Article; Early Access | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.cerifentitytype | Publications | - |
item.languageiso639-1 | en | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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