Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Adrenal Tumor Segmentation on U-Net: A Study About Effect of Different Parameters in Deep Learning
Vietnam Journal of Computer Science · Şubat 2024
YÖKSİS Kayıtları
Adrenal Tumor Segmentation on U-Net: A Study About Effect of Different Parameters in Deep Learning
VIETNAM JOURNAL OF MEDICINE · 2024 ESCI
DOÇENT HAKAN CEBECİ →
Adrenal Tumor Segmentation on U-Net: A Study About Effect of Different Parameters in Deep Learning
Vietnam Journal of Computer Science · 2024 DOAJ: Directory of Open Access Journals
PROFESÖR MUSTAFA KOPLAY →
Makale Bilgileri
DergiVietnam Journal of Computer Science
Yayın TarihiŞubat 2024
Cilt / Sayfa11 · 111-135
Scopus ID2-s2.0-85177083598
Erişim🔓 Açık Erişim
Özet
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.
Yazarlar (5)
1
Ahmet Solak
ORCID: 0000-0002-5494-1987
2
Rahime Ceylan
ORCID: 0000-0003-0294-5692
3
Mustafa Alper Bozkurt
ORCID: 0000-0001-5171-3295
4
Hakan Cebeci
5
M. Koplay
ORCID: 0000-0001-7513-4968
Anahtar Kelimeler
Adrenal tumor
deep learning
parameter analysis
segmentation
U-Net
Kurumlar
Konya Technical University
Konya Turkey
Selçuk Tip Fakültesi
Konya Turkey