MARC ayrıntıları
| 000 -BAŞLIK |
|---|
| Sabit Uzunluktaki Kontrol Alanı |
03574nam a22003017a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ |
|---|
| Kontrol Alanı |
KOHA |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI |
|---|
| Kontrol Alanı |
20230419084605.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ |
|---|
| Sabit Alan |
221104d2022 cy ||||| m||| 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 2455 |
| Cutter no |
L29 2022 |
| 100 1# - KİŞİ ADI |
|---|
| Yazar Adı (Kişi adı) |
Lawal, Misbahu Koramar |
| 245 10 - ESER ADI BİLDİRİMİ |
|---|
| Başlık |
APPLICATION OF TRANSFER LEARNING FOR THE DETECTION OF TUBERCULOSIS FROM MICROSCOPIC SLIDE AND CHEST X-RAY IMAGES / |
| Sorumluluk Bildirimi |
MISHABU KORAMAR BOKO LAWAL; SUPERVISOR: Asst. Prof. Dr. Kamil YURTKAN |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
2022 |
| 300 ## - FİZİKSEL TANIMLAMA |
|---|
| Sayfa, Cilt vb. |
46 sheets; |
| Boyutları |
31 cm. |
| Birlikteki Materyal |
Includes CD |
| 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 Bioengineering Department |
| 504 ## - BİBLİYOGRAFİ NOTU |
|---|
| Bibliyografi Notu |
Includes bibliography (sheets 37-40) |
| 520 ## - ÖZET NOTU |
|---|
| Özet notu |
ABSTRACT<br/>Tuberculosis can be detected from microscopic slides and chest X-ray images. The motivation of the automatic detection systems for tuberculosis is the high cases of tuberculosis. The particular detection problems can be solved by employing an AI-driven model which learn features. In this thesis, an automated detection model is presented using X-ray and microscopic slide images to detect tuberculosis into positive and negative cases. A pretrained Alexnet model is employed. The experiments are done on Chest X-ray dataset made available on Kaggle repository and microscopic slide images from both Federal Medical Center Zamfara and Kaggle repository. For classification of tuberculosis using microscopic slide images, the model recorded 90.56 testing accuracy, 97.78% testing sensitivity and 83.33% testing specificity for 70: 30 training and testing splits, 100% testing accuracy, 100% testing sensitivity and 100% testing specificity for 80:20 split, 96.67% testing accuracy, 93.33% testing sensitivity and 100% testing specificity for 90:10 training and testing split and 94.66% average testing accuracy, 98.33% average testing sensitivity and 91.00 % average testing specificity after cross validation. For classification of tuberculosis using X-ray images, the used method achieved 93.89% testing, 96.67% testing sensitivity and 91.11% testing specificity for 70:30 training and testing splits, 94.17% testing accuracy, 98.33% testing sensitivity and 90% testing specificity for 80:20 training and testing split, 98.33% testing accuracy, 100% testing sensitivity and 96.67% testing specificity for 90:10 training and testing split and 94.00% average testing accuracy, 98.33% average testing sensitivity and 98.33% average testing specificity after cross validation. Our results are following the notion that pretrained convolutional neural network models can take part for categorizing medical images with acceptable accuracy. These particular models can be used as a confirmatory tool for diagnosis of both pneumonia and tuberculosis, augmenting miss diagnosis and bring an option for the higher and monotonous workload faced by specialists, radiologists and pathologists in Federal Medical Centre Zamfara.<br/>Keywords: Chest X-ray, deep learning, microscopic slide, pretrained network, AlexNet, Tuberculosis, transfer learning. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ |
|---|
| Konusal terim veya coğrafi ad |
Deep learning (Machine learning) |
| Alt başlık biçimi |
Dissertations, Academic |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ |
|---|
| Konusal terim veya coğrafi ad |
Tuberculosis |
| Alt başlık biçimi |
Dissertations, Academic |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ |
|---|
| Konusal terim veya coğrafi ad |
Transfer learning (Machine learning) |
| Alt başlık biçimi |
Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI |
|---|
| Yazar Adı (Kişi adı) |
Yurtkan, Kamil |
| İlişkili Terim |
supervisor |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) |
|---|
| Sınıflama Kaynağı |
Dewey Onlu Sınıflama Sistemi |
| Materyal Türü |
Thesis |