Development of a Low-Cost Eye Screening Tool for Early Detection of Diabetic Retinopathy using Deep Neural Network

It has been said that technology used in the lab does not directly transfer to what is done in healthcare. Research on the use of Artificial Intelligence (AI) in the diagnosis of Diabetic Retinopathy (DR) has seen tremendous growth over the last couple of years but it is also true not much of that knowledge has been transferred into practice to benefit patients in need. One reason is that it’s a new frontier with untested technologies and one that is evolving too fast. Also, the Real Healthcare situation can be very complicated presenting itself with numerous challenges starting with strict regulations to variability in populations. A solution that is implementable needs to address all these concerns including ethics, standards, and any security concerns. It is also important to note that, the current state of AI is specialized to only narrow applications and may not scale when presented with problems of varied nature. A case in point is a patient having DR may be suffering from other ailments such as Glaucoma or cataracts. DR has been a leading cause of blindness for millions of people worldwide, hard to detect when it’s treatable and therefore early eye screening is the solution. In this Capstone project, we seek to integrate Artificial Intelligence with other technologies to deliver a low-cost diagnosis to Diabetic Retinopathy at the same time trying to overcome previous impediments to the implementation of mass eye screening

Retinal microaneurysms detection using local convergence index features

Retinal microaneurysms are the earliest clinical sign of diabetic retinopathy disease. Detection of microaneurysms is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this paper, a novel and reliable method for automatic detection of microaneurysms in retinal images is proposed. In the first stage of the proposed method, several preliminary microaneurysm candidates are extracted using a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to intensity and shape descriptors, a new set of features based on local convergence index filters is extracted for each candidate. Finally, the collective set of features is fed to a hybrid sampling/boosting classifier to discriminate the MAs from non-MAs candidates. The method is evaluated on images with different resolutions and modalities (RGB and SLO) using five publicly available datasets including the Retinopathy Online Challenge's dataset. The proposed method achieves an average sensitivity score of 0.471 on the ROC dataset outperforming state-of-the-art approaches in an extensive comparison. The experimental results on the other four datasets demonstrate the effectiveness and robustness of the proposed microaneurysms detection method regardless of different image resolutions and modalities

Retinal microaneurysms detection using local convergence index features

\u3cp\u3eRetinal microaneurysms (MAs) are the earliest clinical sign of diabetic retinopathy disease. Detection of MAs is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this paper, a novel and reliable method for automatic detection of MAs in retinal images is proposed. In the first stage of the proposed method, several preliminary microaneurysm candidates are extracted using a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to intensity and shape descriptors, a new set of features based on local convergence index filters is extracted for each candidate. Finally, the collective set of features is fed to a hybrid sampling/boosting classifier to discriminate the MAs from non-MAs candidates. The method is evaluated on images with different resolutions and modalities (color and scanning laser ophthalmoscope) using six publicly available data sets including the retinopathy online challenges (ROC) data set. The proposed method achieves an average sensitivity score of 0.471 on the ROC data set outperforming state-of-the-art approaches in an extensive comparison. The experimental results on the other five data sets demonstrate the effectiveness and robustness of the proposed MAs detection method regardless of different image resolutions and modalities.\u3c/p\u3

Retinal microaneurysms detection using local convergence index features

Retinal microaneurysms (MAs) are the earliest clinical sign of diabetic retinopathy disease. Detection of MAs is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this paper, a novel and reliable method for automatic detection of MAs in retinal images is proposed. In the first stage of the proposed method, several preliminary microaneurysm candidates are extracted using a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to intensity and shape descriptors, a new set of features based on local convergence index filters is extracted for each candidate. Finally, the collective set of features is fed to a hybrid sampling/boosting classifier to discriminate the MAs from non-MAs candidates. The method is evaluated on images with different resolutions and modalities (color and scanning laser ophthalmoscope) using six publicly available data sets including the retinopathy online challenges (ROC) data set. The proposed method achieves an average sensitivity score of 0.471 on the ROC data set outperforming state-of-the-art approaches in an extensive comparison. The experimental results on the other five data sets demonstrate the effectiveness and robustness of the proposed MAs detection method regardless of different image resolutions and modalities

Análisis de retinografías basado en Deep Learning para la ayuda al diagnóstico de la retinopatía diabética

La Retinopatía Diabética (RD) es una complicación de la diabetes y es la causa más frecuente de ceguera en la población laboral activa de los paísesdesarrollados. Cuando se trata de forma precoz, la pérdida de visión se puede prevenir. Para ello, es necesario que los pacientes se sometan aexámenes oftalmológicos regulares en los que se capturan y analizan imágenes de su fondo ocular o retinografías. No obstante, la creciente incidencia dela diabetes y la falta de profesionales sanitarios dificultan la detección precoz de la RD. En este contexto, los sistemas automáticos de ayuda aldiagnóstico de la RD ofrecen beneficios en escenarios clínicos y de cribado. En este TFM se pretende contribuir a esta tarea mediante el desarrollo de unmétodo automático de procesado de retinografías basado en técnicas de deep learning. Para ello se empleará el lenguaje de programación Python y setrabajará con retinografías procedentes de un contexto clínico real. Asimismo, la alumna tendrá la oportunidad de trabajar en un grupo de investigaciónmultidisciplinar, colaborando con ingenieros y médicos especialistas en oftalmología del Hospital Clínico Universitario de Valladolid.Sight is one of the most important senses for human beings. In recent years, the number of eye diseases has increased considerably and the same trend is expected in the coming years. Some of them, such as diabetic retinopathy, glaucoma or cataracts, have become major causes of vision loss worldwide. The alterations they cause in the human eye can be seen using digital images, such as fundus images. This technique is very common and useful for the diagnosis of this type of pathologies. Early detection is key to prevent the disease from reaching its most advanced stages and to make treatment more effective. Therefore patients should undergo frequent ophthalmological examinations. However, the increasing incidence of some diseases and the shortage of specialist ophthalmologists make the manual analysis of retinal images a complex and time-consuming task. In this context, automated screening systems can be very useful to assist ophthalmologists. Despite the great effectiveness of Deep learning-based systems, their application in clinical practice is still not very evident, as a consequence of their "black box" nature. In order to solve this problem, Explainable Artificial Intelligence (XAI) has been developed, a set of techniques that try to explain the decisions made by computational models when they are used for a specific task.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaMáster en Ingeniería de Telecomunicació