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Yüksek Parlaklık Kuantum Nokta Led Aygıtların Geliştirilmesi
(Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü, 2023) Biçer, Ayşenur; Mutlugün, Evren
Optoelectronic devices are essential components of optical communication systems, internet and displays. Among these devices, in the category of light emitting diodes (LED), there are quantum dot LEDs (QLED) that emit light by employing quantum dots (QDs) and have rich optoelectronic properties such as varying emission wavelength associated with the its size and excellent brightness [1], [2]. In this thesis, we worked on transparent and solution processible QLEDs in three groups: Indium Phosphide (InP) QLEDs, Carbon Quantum Dot (CQD) LEDs and Cadmium Selenide (CdSe) QLEDs. In the InP study, a QLED was fabricated using InP-based QDs as the emitting layer to demonstrate the feasibility of these QDs. Results found a maximum external quantum efficiency (EQE) of 1.16% and brightness of 1039 cd/m2. For the CQD LEDs, yellow emissive QDs were mixed systematically in Poly(9-vinylcarbazole) (PVK) as the host. A blue-to-white shift was observed in the CIE coordinate with varying ratios. From these, white luminescent devices were obtained with a maximum brightness of 774.3 cd/m2 and an EQE of 0.76%. High-brightness irradiation was obtained compared to other white-luminescent studies in the literature. In CdSe QLEDs, as a proof of concept, devices with a maximum brightness of 111,450 cd/m2 and an EQE of 15.08% were obtained. In these three works, devices with high brightness in their own categories were produced using both heavy metal and non-heavy metal QDs. Keywords: Optoelectronics, LED, QD, CQD LED, InP QLED, CdSe QLED
Derin Öğrenme Yöntemleri Kullanarak Dermatoskopik Görüntülerden Otomatik Cilt Kanseri Tespiti ve Sınıflandırılması
(Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü, 2023) Kalaycı, Serdar; Yılmaz, Bülent
Early detection of skin cancer is crucial for successful treatment and improved patient outcomes. The most prevalent form of cancer is skin cancer and if left undetected, it can spread and become more difficult to treat. A dangerous and frequently fatal type of skin cancer is melanoma. Regular skin examinations and self-examinations can help identify suspicious moles or lesions, which can then be evaluated by a dermatologist. In addition, advances in technology and artificial intelligence have enabled the development of tools for automated skin cancer screening, providing a convenient and efficient means of early detection. This can lead to more efficient diagnosis, reduced healthcare costs and improved patient care. By evaluating skin lesions from images, deep learning techniques have shown considerable potential in increasing the precision of melanoma detection. By using large datasets and complex neural networks, deep learning algorithms can effectively distinguish between benign and malignant skin lesions with high accuracy. Ensemble of CNN models helps improve the performance and reliability of the classification task. By combining the predictions of multiple CNN models lead to more accurate and robust predictions. In this thesis, for melanoma classification problem, many different data augmentations techniques applied and different convolutional neural networks architectures evaluated, applied vignetting effect filter and hair noise in accordance with the dataset and results of ensemble of the best CNN models are promising. This thesis attempts to produce a reliable model for the classification of melanoma by conducting experiments on two combined publically accessible data sets, ISIC 2019 and ISIC 2020. On the testing sets in our studies, the proposed solution attained 95.75% AUC.

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