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Öğe Block Attention and Switchable Normalization Based Deep Learning Framework for Segmentation of Retinal Vessels(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Deari, Sabri; Oksuz, Ilkay; Ulukaya, SezerThe presence of high blood sugar levels damages blood vessels and causes an eye condition called diabetic retinopathy. The ophthalmologist can detect this disease by looking at the variations in retinal blood vasculature. Manual segmentation of vessels requires highly skilled specialists, and not possible for many patients to be done quickly in their daily routine. For these reasons, it is of great importance to isolate retinal vessels precisely, quickly, and accurately. The difficulty distinguishing the retinal vessels from the background, and the small number of samples in the databases make this segmentation problem difficult. In this study, we propose a novel network called Block Feature Map Distorted Switchable Normalization U-net with Global Context Informative Convolutional Block Attention Module (BFMD SN U-net with GCI- CBAM). We improve the traditional Fully Convolutional Segmentation Networks in multiple aspects with the proposed model as follows; The model converges in earlier epochs, adapts more flexibly to different data, is more robust against overfitting, and gets better feature refinement at different dilation rates to cope with different sizes of retinal vessels. We evaluate the proposed network on two reference retinal datasets, DRIVE and CHASE DB1, and achieve state-of-the-art performances with 97.00 % accuracy and 98.71 % AUC in DRIVE and 97.62 % accuracy and 99.11 % AUC on CHASE DB1 databases. Additionally, the convergence step of the model is reduced and it has fewer parameters than the baseline U-net. In summary, the proposed model surpasses the U-net -based approaches used for retinal vessel separation in the literature.Öğe A Hybrid Fusion Method Combining Spatial Image Filtering with Parallel Channel Network for Retinal Vessel Segmentation(Springer Heidelberg, 2023) Yakut, Cem; Oksuz, Ilkay; Ulukaya, SezerRetinography is a frequently used imaging method that aids in the clinical diagnosis of eye disorders. Low contrast and being exposed to noise are the primary factors in degraded retinal fundus images. These factors make it challenging for medical experts to diagnose and classify diseases in retinal images. This manuscript proposes a hybrid fusion approach for vascular tree segmentation in color fundus images. We propose to use a fusion model that combines supervised deep convolutional neural networks with unsupervised approaches. The training fundus images were preprocessed in an unsupervised way to increase the success of the deep U-Net architecture and fed into the network as parallel channels. Preprocessing steps include the following stages: grayscale conversion, median filtering, CLAHE, mathematical morphology operations, Coye filtering, connected component analysis, and data augmentation. The proposed approach was tested on publicly accessible DRIVE and HRF datasets. Sensitivity, specificity, accuracy, and F1-score measures are compared on high and low-resolution datasets. In summary, results reveal that the performance of the parallel channel-based deep approach is higher than the baseline deep model and achieved state-of-the-art results in the literature, especially on the HRF dataset. Besides, the fusion of the predictions of only the unsupervised image processing-based models achieved the best accuracy among unsupervised works in the literature on the DRIVE dataset. Moreover, the proposed unsupervised preprocessing-based approach does not add a significant computational burden on the deep learning model training. Additionally, the proposed hybrid method has noticeably increased the sensitivity rate on both datasets.Öğe Importance of Data Augmentation and Transfer Learning on Retinal Vessel Segmentation(IEEE, 2021) Deari, Sabri; Oksuz, Ilkay; Ulukaya, SezerAutomatic segmentation of retinal fundus images for extracting blood vessels is an essential task in the diagnostic classification of hypertension, glaucoma, and diabetic retinopathy, which are the leading causes of blindness. In this paper, we employed a transfer learning strategy for improved retinal vessel extraction. Firstly, we trained the U-NET model on CHASE DB1 and DRIVE databases. By using data augmentations on datasets we enable the U-NET model to learn retinal vessel features better. We examined the data augmentation types, namely, pixel-level transformations and affine transformations. Secondly, we utilized the transfer learning approach on two datasets and achieved comparable results with the state-of-the-art studies on retinal vessel segmentation task. Also, we employed combination of affine and pixel-level transformations to further boost segmentation performance.