SKIN CANCER IDENTIFICATION THROUGH DEEP ENSEMBLE LEARNING / (Kayıt no. 293019)
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000 -BAŞLIK | |
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Sabit Uzunluktaki Kontrol Alanı | 02248nam a22002657a 4500 |
003 - KONTROL NUMARASI KİMLİĞİ | |
Kontrol Alanı | KOHA |
005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
Kontrol Alanı | 20250110133050.0 |
008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
Sabit Alan | 240927d2024 cy ode|| |||| 00| 0 eng d |
040 ## - KATALOGLAMA KAYNAĞI | |
Özgün Kataloglama Kurumu | CY-NiCIU |
Kataloglama Dili | eng |
Çeviri Kurumu | CY-NiCIU |
Açıklama Kuralları | rda |
041 ## - DİL KODU | |
Metin ya da ses kaydının dil kodu | eng |
090 ## - Yerel Tasnif No | |
tasnif no | YL 3500 |
Cutter no | E77 2024 |
100 1# - KİŞİ ADI | |
Yazar Adı (Kişi adı) | Ersuz, Cemaliye |
245 10 - ESER ADI BİLDİRİMİ | |
Başlık | SKIN CANCER IDENTIFICATION THROUGH DEEP ENSEMBLE LEARNING / |
Sorumluluk Bildirimi | CEMALİYE ERSUZ ; SUPERVISOR, ASST. PROF. DR. EMRE ÖZBİLGE |
264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
300 ## - FİZİKSEL TANIMLAMA | |
Sayfa, Cilt vb. | 61 sheets ; |
Boyutları | 30 cm |
Birlikteki Materyal | +1 CD ROM |
336 ## - CONTENT TYPE | |
Source | rdacontent |
Content type term | text |
Content type code | txt |
337 ## - MEDIA TYPE | |
Source | rdamedia |
Media type term | unmediated |
Media type code | n |
338 ## - CARRIER TYPE | |
Source | rdacarrier |
Carrier type term | volume |
Carrier type code | nc |
502 ## - TEZ NOTU | |
Tez Notu | Thesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering |
520 ## - ÖZET NOTU | |
Özet notu | Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by unrepaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer is prone to spread over other parts of the body so it can be better treated in early stages and that's why you should detect it at an early stage. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. To take into account the seriousness of these matters, several early detection techniques for skin cancer have been identified by researchers. Parameters such as symmetry, color, size, and shape are used to identify skin cancer and differentiate benign skin cancer from melanoma. This master thesis researches the utilization of skin cancer identification through deep ensemble learning using given dataset images. The research evaluates how effectively the combination of a lot of deep learning models, such as convolutional neural networks (CNNs), through transfer learning and Ensemble learning. Machine learning is the main operation of the Convolutional Neural Network which is used as a deep neural networks model like ResNet152V2, MobileNetV2, VGG19, DenseNet201, and InceptionV3 for image classification and identification. |
650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
Konusal terim veya coğrafi ad | Computer Engineering |
Alt başlık biçimi | Dissertations, Academic |
700 1# - EK GİRİŞ - KİŞİ ADI | |
Yazar Adı (Kişi adı) | Özbilge, Emre |
İlişkili Terim | supervısor |
942 ## - EK GİRİŞ ÖGELERİ (KOHA) | |
Sınıflama Kaynağı | Dewey Onlu Sınıflama Sistemi |
Materyal Türü | Thesis |
Geri Çekilme Durumu | Kayıp Durumu | Sınıflandırma Kaynağı | Kredi için değil | Koleksiyon Kodu | Kalıcı Konum | Mevcut Konum | Raf Yeri | Kayıt Tarih | Source of acquisition | Toplam Ödünçverme | Yer Numarası | Demirbaş Numarası | Son Görülme Tarihi | Kopya Bilgisi | Fatura Tarihi | Materyal Türü | Genel / Bağış Notu |
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Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Depo | 25.10.2024 | Bağış | YL 3500 E77 2024 | T3947 | 25.10.2024 | C.1 | 25.10.2024 | Thesis | Computer Engineering | ||||
Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 25.10.2024 | Bağış | YL 3500 E77 2024 | CDT3947 | 25.10.2024 | C.1 | 25.10.2024 | Suppl. CD | Computer Engineering |