Multidataset Incremental Training for Optic Disc Segmentation

Civit-Masot, Javier; Billis, Antonis; Dominguez-Morales, MJ; Vicente-Díaz, Saturnino; Balcells, A

doi:10.1007/978-3-030-48791-1_28

Cited by 2 publications

(1 citation statement)

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“…U-net is widely used fully convolutional network that has been widely used for medical image segmentation. This part of the architecture is fully described in [28].…”

Section: D: Segmentation Networkmentioning

confidence: 99%

Dual Machine-Learning System to Aid Glaucoma Diagnosis Using Disc and Cup Feature Extraction

et al. 2020

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Glaucoma is a degenerative disease that affects vision, causing damage to the optic nerve that ends in vision loss. The classic techniques to detect it have undergone a great change since the intrusion of machine learning techniques into the processing of eye fundus images. Several works focus on training a convolutional neural network (CNN) by brute force, while others use segmentation and feature extraction techniques to detect glaucoma. In this work, a diagnostic aid tool to detect glaucoma using eye fundus images is developed, trained and tested. It consists of two subsystems that are independently trained and tested, combining their results to improve glaucoma detection. The first subsystem applies machine learning and segmentation techniques to detect optic disc and cup independently, combine them and extract their physical and positional features. The second one applies transfer learning techniques to a pre-trained CNN to detect glaucoma through the analysis of the complete eye fundus images. The results of both systems are combined to discriminate positive cases of glaucoma and improve final detection. The results show that this system achieves a higher classification rate than previous works. The system also provides information on the basis for the proposed diagnosis suggestion that can help the ophthalmologist to accept or modify it. INDEX TERMS Glaucoma, Ensemble networks, Medical diagnostic aids, medical imaging, explainable AI.

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“…U-net is widely used fully convolutional network that has been widely used for medical image segmentation. This part of the architecture is fully described in [28].…”

Section: D: Segmentation Networkmentioning

confidence: 99%