Doktora Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/5800
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Doctoral Thesis MRG Taramalarında Alzheimer Hastalığının Zaman Dağılımlı Sınıflandırılması(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Dündar, Mehmet Sait; Yılmaz, BülentThis thesis presents a comprehensive framework for studying Alzheimer's Disease (AD) progression by focusing on the classification of AD, Mild Cognitive Impairment (MCI), and Cognitively Normal (CN) individuals using advanced machine learning models that analyze changes in brain volumetrics over time through MRI scans. In the first phase of the research, MR images from the Alzheimer's Disease Neuroimaging Initiative database were utilized, which included sequences of 3-4 scans taken annually from 22 CN, 18 AD, and 20 MCI subjects. Key volumetric parameters such as cortical thickness and intracranial volumes were extracted using the CAT12 toolbox in SPM software. A novel classification method based on the rate of volumetric changes over time was employed, effectively capturing the progressive nature of neurological changes. This approach achieved accuracies of 82.5% in distinguishing AD from CN, 71% in differentiating MCI from AD, and 69% in separating MCI from CN, alongside a 55% accuracy in a three-way classification using random forest and support vector machines. Building on these initial insights, the second phase of the study significantly advanced the methodology by integrating a pre-trained 3D ResNet 101 CNN algorithm for initial spatial categorization of MRI scans, followed by the use of Long Short-Term Memory (LSTM) networks. These LSTMs processed the same sequences of 3-4 annual scans for each patient, enhancing the model's ability to analyze and interpret the temporal progression of volumetric changes. This sophisticated approach led to marked improvements in classification accuracy: 96.7% in differentiating AD from CN, 87.5% in distinguishing AD from MCI, and 86.4% in separating MCI from CN. The study effectively demonstrates a significant enhancement in capturing the temporal dynamics of AD progression.Doctoral Thesis Biyoçipler için Mikro Biyomalzemelerin ve Hücrelerin Görüntü İşleme Yöntemleri ile Otomatik Olarak Sayılması ve Analizi(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Çelebi, Fatma; İçöz, KutayQuantification of tumor cells is essential for early cancer detection and progression tracking. Multiple techniques have been devised to detect tumor cells. In addition to conventional laboratory instruments, several biochip-based techniques have been devised for this purpose. Our biochip design incorporates micron-sized immunomagnetic beads and micropad arrays, necessitating automated detection and quantification not only of cells but also of the micropads and immunomagnetic beads. The primary function of the biochip is to simultaneously acquire target cells with distinct antigens. As a readout technique for the biochip, this study devised a digital image processing-based method for quantifying leukemia cells, immunomagnetic beads, and micropads. Images were acquired on the chip using bright-field microscopy with image objectives of 20X and 40X. Conventional image processing methods, machine learning methods, and deep learning methods were used to analyze the images. To quantify targets in the images captured by a bright-field microscope, color- and size-based object recognition and machine learning-based methods were first implemented. Secondly, color- and size-based object detection and object segmentation methods were implemented to detect structures in bright-field optical microscope images acquired from the biochip. Third, segmentation of the minimal residual disease (MRD) using deep learning. Implemented biochip images comprised of leukemic cells, immunomagnetic beads, and micropads. Moreover, mesenchymal stem cells (MSCs) are stem cells with the capacity for multilineage differentiation and self-renewal. Estimating the proportion of senescent cells is therefore essential for clinical applications of MSCs. In this study, a self-supervised learning (SSL)-based method for segmenting and quantifying the density of cellular senescence was implemented, which can perform well despite the small size of the labeled dataset.Doctoral Thesis Zamansal Bilgiden Faydalanarak Videodan Orman Yangınlarının Erken Tespiti(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Taş, Merve; Taşdemir, Kasım; Aydın, ZaferForest fires are considered as the major threats to lives, properties and to the integrity of the ecosystem around the world. In most cases, the fire damage can be reduced, when the initial signs of the fire are detected in a timely manner. Since smoke is considered as the first visual sign of fire, detection of smoke is vital. Hence, a successfully designed smoke detection system is essentially critical in the early detection of smoke for outdoor environments. The existing smoke detection methods suffer from high false alarm rates and cannot accurately detect smoke in hazy environments. To address these problems, this thesis is focused on smoke detection model at an early stage that utilizes deep learning (DL) based techniques for outdoor locations. This work contributes mainly to four aspects of smoke detection: (1) new datasets preparation for three smoke detection tasks classification, detection-segmentation, and video classification, (2) utilizing transfer learning to detect the smoke on the relatively small dataset, (3) image dehazing process that includes removing the haze from the dataset images to enhance the system performance, (4) designing a novel hybrid video classification model by combining the two DL based video classification structures. This work will be a resourceful reference for researchers working in the fields of forest fire or smoke detection studies at an early stage. The experiments, research findings, and enhanced performance of the smoke detection system provide a source of information about smoke detection. Current studies can be utilized to further improve the design of efficient and reliable fire safety models. Keywords: Deep Learning, Spatio-Temporal Information, Forest Fire Early Detection, Smoke Detection, Image Dehazing