Yasemin Turkan

Alzheimer OCT (AOCT) Veri Kümesi

Yasemin Turkan, Fatıh Arpaci, Ozan Arslan, Büşra Şahin et autres

Alzheimer-OCT Veri Kümesi (AOCT), 2023–2025 yılları arasında Antalya Eğitim ve Araştırma Hastanesi’nde yürütülen prospektif bir çalışma kapsamında toplanmış olup; gerekli etik onay Koç Üniversitesi Klinik Ara¸stırmalar Etik Kurulu’ndan alınmı¸s (Tarih: 01.03.2023, Karar No: 2023.071.IRB1.023) ve tüm süreç Helsinki Bildirgesi standartlarına uygun olarak …

Alzheimer-OCT Dataset and Detection of Alzheimer’s Disease using Deep Learning

Büşra ŞAHİN, Yasemin Turkan, Fatıh Arpaci, Ozan Arslan et autres

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Self-Supervised Learning of 3D Structure from 2D OCT Slices for Retinal Disease Diagnosis on UK Biobank Scans

Muhammet Serdar Nazlı, Yasemin Turkan, F. Boray Tek

This study presents a self-supervised learning framework for retinal disease classification using Optical Coherence Tomography (OCT) scans. To balance the contextual richness of 3D volumes with the computational efficiency of 2D architectures, we introduce a quasi-3D input generation strategy. Each input is …

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Retinal Disease Classification from Bimodal OCT and OCTA Using a CNN–ViT Hybrid Architecture

Ömer Faruk Aydın, F. Boray Tek, Yasemin Turkan

Retinal diseases are the leading cause of vision impairment and blindness worldwide. Early and accurate diagnosis is critical for effective treatment, and recent advances in imaging technologies such as Optical Coherence Tomography (OCT) and OCT Angiography (OCTA), have enabled detailed visualization of …

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Retinal Disease Diagnosis in OCT Scans Using a Foundational Model

Muhammet Serdar Nazlı, Yasemin Turkan, F. Boray Tek, Devrım Toslak et autres

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Retinal Disease Classification Using Optical Coherence Tomography Angiography Images

Omer Aydin, Muhammet Serdar Nazlı, F. Boray Tek, Yasemin Turkan

Optical Coherence Tomography Angiography (OCTA) is a non-invasive imaging modality widely used for the detailed visualization of retinal microvasculature, which is crucial for diagnosing and monitoring various retinal diseases. However, manual interpretation of OCTA images is labor-intensive and prone to variability, highlighting …

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Segmentation Based Classification of Retinal Diseases in OCT Images

Öykü Eren, F. Boray Tek, Yasemin Turkan

Volumetric optical coherence tomography (OCT) scans offer detailed visualization of the retinal layers, where any deformation can indicate potential abnormalities. This study introduced a method for classifying ocular diseases in OCT images through transfer learning. Applying transfer learning from natural images to …

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Automated Diagnosis of Alzheimer’s Disease Using OCT and OCTA: A Systematic Review

Yasemin Turkan, F. Boray Tek, Fatıh Arpaci, Ozan Arslan et autres

Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) have emerged as promising, non-invasive, and cost-effective modalities for the early diagnosis of Alzheimer’s disease (AD). However, a comprehensive review of automated deep learning techniques for diagnosing AD or mild cognitive …

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Automated diagnosis of AD using OCT and OCTA: A systematic review

Yasemin Turkan, F. Boray Tek

Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) are promising tools for the early-stage diagnosis of Alzheimer’s disease (AD). These non-invasive imaging techniques are cost-effective and more accessible than alternative neuroimaging tools. However, the current literature lacks an extensive …

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A Survey on Automated Diagnosis of Alzheimer's Disease Using Optical Coherence Tomography and Angiography

Yasemin Turkan, F. Boray Tek

Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's disease (AD). These non-invasive imaging techniques are cost-effective and more accessible than alternative neuroimaging tools. However, interpreting and classifying multi-slice scans produced …

Convolutional Attention Network for MRI-based Alzheimer’s Disease Classification and its Interpretability Analysis

Yasemin Turkan, F. Boray Tek

Neuroimaging techniques, such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), help to identify Alzheimer’s disease (AD). These techniques generate large-scale, high-dimensional, multimodal neuroimaging data, which is time-consuming and difficult to interpret and classify. Therefore, interest in deep learning approaches …

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Using Adaptive Locally Connected Layer in Attention Based Deep Neural Network for Speech Command Recognition

Yasemin Turkan, F. Boray Tek

Speech command recognition is an active research topic associated with the human-machine interface. Such problems can be successfully solved with attention-based deep networks. In this study, we improved one of the existing attentionbased deep network methods by using an adaptive locally connected …

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