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http://hdl.handle.net/10609/113126
Title: Reconeixement intel·ligent de malalties oculars mitjançant arquitectures d'aprenentatge profund
Author: Coll Corbilla, Jordi
Director: Nuñez Do Rio, Joan Manuel
Tutor: Ventura Royo, Carles  
Keywords: convolutional neural networks
image classification
deep learning
retinography
medical imaging analysis
eye diseases
Issue Date: 31-Jan-2019
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: Retinal pathologies are the most common cause of childhood blindness worldwide. Rapid and automatic detection of diseases is critical and urgent in reducing the ophthalmologist's workload. Ophthalmologists diagnose diseases based on pattern recognition through direct or indirect visualization of the eye and its surrounding structures. Dependence on the fundus of the eye and its analysis make the field of ophthalmology perfectly suited to benefit from deep learning algorithms. Each disease has different stages of severity that can be deduced by verifying the existence of specific lesions and each lesion is characterized by certain morphological features where several lesions of different pathologies have similar characteristics. We note that patients may be simultaneously affected by various pathologies, and consequently, the detection of eye diseases has a multi-label classification with a complex resolution principle. Two deep learning solutions are being studied for the automatic detection of multiple eye diseases. The solutions chosen are due to their higher performance and final score in the ILSVRC challenge: GoogLeNet and VGGNet. First, we study the different characteristics of lesions and define the fundamental steps of data processing. We then identify the software and hardware needed to execute deep learning solutions. Finally, we investigate the principles of experimentation involved in evaluating the various methods, the public database used for the training and validation phases, and report the final detection accuracy with other important metrics.
Language: Catalan
URI: http://hdl.handle.net/10609/113126
Appears in Collections:Bachelor thesis, research projects, etc.

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