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Yayın Segmentation based classification of retinal diseases in OCT images(Institute of Electrical and Electronics Engineers Inc., 2024) Eren, Öykü; Tek, Faik Boray; Turkan, YaseminVolumetric 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 Optical Coherence Tomography (OCT) scans present challenges, particularly when target domain examples are limited. Our approach aimed to enhance OCT-based retinal disease classification by leveraging transfer learning more effectively. We hypothesize that providing an explicit layer structure can improve classification accuracy. Using the OCTA-500 dataset, we explored various configurations by segmenting the retinal layers and integrating these segmentations with OCT scans. By combining horizontal and vertical cross-sectional middle slices and their blendings with segmentation outputs, we achieved a classification a ccuracy of 91.47% and an Area Under the Curve (AUC) of 0.96, significantly outperforming the classification of OCT slice images.Yayın Retinal disease classification using optical coherence tomography angiography images(Institute of Electrical and Electronics Engineers Inc., 2024) Aydın, Ömer Faruk; Nazlı, Muhammet Serdar; Tek, Faik Boray; Turkan, YaseminOptical 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 the need for automated classification methods. This study presents an aproach that utilizes transfer learning to classify OCTA images into different retinal disease categories, including age-related macular degeneration (AMD) and diapethic retinopathy (DR). We used the OCTA-500 dataset [1], the largest publicly available retinal dataset that contains images from 500 subjects with diverse retinal conditions. To address the class imbalance, we employed k-fold cross-validation and grouped various other conditions under the 'OTHERS' class. Additionally, we compared the performance of the ResNet50 model with OCTA inputs to that of the ResNet50 and RetFound (Vision Transformer) models with OCT inputs to assess the efficiency of OCTA in retinal condition classification. In the three-class (AMD, D R, Normal) classification, ResNet50-OCTA o utperformed ResNet50-OCT, but slightly underperformed compared to RetFound-OCT, which was pretrained on a large OCT dataset. In the four-class (AMD, DR, Normal, Others) classification, ResNet50-OCTA and RetFound-OCT achieved similar classification a ccuracies. This study establishes a baseline for retinal condition classification using the OCTA-500 dataset and provides a comparison between OCT and OCTA input modalities.Yayın Automated diagnosis of Alzheimer’s Disease using OCT and OCTA: a systematic review(Institute of Electrical and Electronics Engineers Inc., 2024-08-06) Turkan, Yasemin; Tek, Faik Boray; Arpacı, Fatih; Arslan, Ozan; Toslak, Devrim; Bulut, Mehmet; Yaman, AylinRetinal 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 impairment (MCI) using OCT/OCTA data is lacking. We addressed this gap by conducting a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We systematically searched databases, including Scopus, PubMed, and Web of Science, and identified 16 important studies from an initial set of 4006 references. We then analyzed these studies through a structured framework, focusing on the key aspects of deep learning workflows for AD/MCI diagnosis using OCT-OCTA. This included dataset curation, model training, and validation methodologies. Our findings indicate a shift towards employing end-to-end deep learning models to directly analyze OCT/OCTA images in diagnosing AD/MCI, moving away from traditional machine learning approaches. However, we identified inconsistencies in the data collection methods across studies, leading to varied outcomes. We emphasize the need for longitudinal studies on early AD and MCI diagnosis, along with further research on interpretability tools to enhance model accuracy and reliability for clinical translation.Yayın Retinal disease classification from bimodal OCT and OCTA using a CNN-ViT hybrid architecture(Institute of Electrical and Electronics Engineers Inc., 2025-09-21) Aydın, Ömer Faruk; Tek, Faik Boray; Turkan, YaseminRetinal 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 the retinal structure and vasculature. By leveraging these modalities, this study proposes an advanced deep learning architecture called MultiModalNet for automated multi-class retinal disease classification. MultiModalNet employs a dual-branch design, where OCTA projection maps are processed through a ResNet101 encoder, and cross-sectional slices from the OCT volume (B-scans) are analyzed using a Vision Transformer (ViT-Large). The extracted features from both branches were fused and passed through the fully connected layers for the final classification. Evaluated on the 3-class OCTA-500 dataset, which includes Age-related Macular Degeneration (AMD), Diabetic Retinopathy (DR), and Normal cases, the proposed model achieved state-of-the-art classification accuracy of 94.59 percent, significantly o utperforming single-modality baselines. This result highlights the effectiveness of integrating vascular and structural information to improve the diagnostic performance. The findings suggest that hybrid multi-modal deep learning approaches can play a transformative role in computer-aided ophthalmology, enhancing both clinical decision-making and screening workflows.
