BRAIN TUMOR CLASSIFICATION USING DEEP LEARNING: A COMPARATIVE ANALYSIS OF VGG19, EFFICIENTNETB3, AND RESNET50 PERFORMANCE / (Kayıt no. 292854)
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| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 02580nam a22002657a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20241007144235.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 240925d2024 cy dj||| |||| 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 3427 |
| Cutter no | H36 2024 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Hamed, Kamila Abdulhamıd Saleh |
| 245 10 - ESER ADI BİLDİRİMİ | |
| Başlık | BRAIN TUMOR CLASSIFICATION USING DEEP LEARNING: A COMPARATIVE ANALYSIS OF VGG19, EFFICIENTNETB3, AND RESNET50 PERFORMANCE / |
| Sorumluluk Bildirimi | KAMILA ABDULHAMID SALEH HAMED ; SUPERVISOR, ASSOC. PROF. DR. UMAR ÖZGÜNALP |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 51 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 Electrical and Electronic Engineering |
| 520 ## - ÖZET NOTU | |
| Özet notu | In our current times, with the alarming spread of brain tumors of various types and<br/>causes, the development and improvement of deep models to expedite and simplify<br/>the process of detecting and classifying brain tumors is of utmost importance.<br/>Therefore, in this study, we conduct a comparative analysis of three pretrained deep<br/>learning models VGG19, EfficientNetB3, and ResNet50 with the BTMRI dataset<br/>from Kaggle. The models were implemented and trained using the Google Colab<br/>environment. The evaluation of the models is based on their validation accuracy in<br/>classifying brain tumor images. Initially, the models were trained on this BTMRI<br/>dataset to evaluate their performances which demonstrated varying levels with<br/>validation accuracies of 93.89% for VGG19, 99.09% for EfficientNetB3, and 98%<br/>for ResNet50. Then the performance of these models was enhanced using the Optuna<br/>hyperparameters optimization library to tune three main hyperparameters learning<br/>rate, dropout rate, and number of epochs, due to this the models achieved higher<br/>validation accuracy rates of 95.42% for VGG19, 99.73% for EfficientNetB3, and<br/>99.54% for ResNet50. Overall This study highlights the effectiveness of deep<br/>learning models in medical image classification and the impact of hyperparameter<br/>optimization in improving model performance. These findings could guide future<br/>research and practical applications in the field of medical diagnostics, contributing to<br/>the detection of brain tumors more accurately and efficiently. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Electrical and Electronic Engineering |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Özgünalp, Umar |
| İlişkili Terim | supervisor |
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Depo | 25.09.2024 | Bağış | YL 3427 H36 2024 | T3844 | 25.09.2024 | C.1 | 25.09.2024 | Thesis | Electrical and Electronic Engineering | ||||
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 25.09.2024 | Bağış | YL 3427 H36 2024 | CDT3844 | 25.09.2024 | C.1 | 25.09.2024 | Suppl. CD | Electrical and Electronic Engineering |