AI-Powered Segmentation of Retinal Fundus Images: Optic Disc and Optic Cup Focus
Özet
Accurate segmentation of optic disc (OD) and optic cup (OC) from retinal fundus images plays a crucial role in the early diagnosis and treatment of eye diseases that can lead to irreversible eye damages and blindness, such as glaucoma and diabetic retinopathy, and other diseases. In addition to image processing methods such as thresholding, edge detection, region-based methods for segmentation of these structures, machine learning (ML) based methods have been added in recent years. With deep learning (DL)-based methods, these segmentation processes are much more successful and faster than conventional methods. Also they can be performed independently of clinical conditions. In this book we investigate deep learning methods for segmentation OD and OC structures from retinal fundus images. In addition, in the later parts of the book, a new deep learning model inspired by the U-Net architecture, one of the most popular models in image segmentation, is presented. The proposed model includes modified channel attention, modified spatial attention mechanisms and modified ConvMixer architectures. While it is aimed to perform a more precise segmentation process with the help of the attention mechanism methods, it is aimed to perform these operations both faster and with higher accuracy with the help of the ConvMixer structure. To validate our proposal, we used the DDRD Net dataset, which was created from four different popular fundus datasets. The dataset is publicly available and was created by labeling 1310 colored and equally sized retinal fundus images by experts. The evaluated experiment results show that the proposed model outperforms 8 state-of-the-art models (UNet, SegNet, HRNet, DeeplabV3+, ENet, PSPNet, ICNet, FCN8) which gives the most successful results in image segmentation in terms of all obtained metrics. Obtained results show that the proposed model can be used effectively in the field of ophthalmology to segment the OD and OC on the retinal fundus images.
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