9 research outputs found
Técnicas de Adquisición de Imágenes de Fondo de Ojo
No full textPresentación realizada en el marco del Proyecto PINV18-846: Detección automática de retinopatía diabética utilizando algoritmos neuro-evolutivos. Cuyo objetivo general fue la Detección Automática de Retinopatía Diabética utilizando algoritmos de aprendizaje neuro-evolutivos.CONACYT - Consejo Nacional de Ciencias y TecnologíaPROCIENCI
Dataset from fundus images for the study of diabetic retinopathy
No full textThis database containing 1437 color fundus images that were acquired at the Department of Ophthalmology of the Hospital de Clínicas, Facultad de Ciencias Médicas, Universidad Nacional de Asunción, Paraguay.
The acquisition of retinal images was done taking into account a clinical procedure. The acquisition of the retinographies was made through the Visucam 500 camera of the Zeiss brand. Expert ophthalmologists have classified the dataset. These data can help doctors and researchers in the detection of cases of Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR), in their different stages.
The classification of fundus images have been done in 7 categories: 1. No DR signs (711 images), 2. Mild (or early) NPDR (6 images), 3. Moderate NPDR (110 images), 4. Severe NPDR (210 images), 5. Very Severe NPDR (139 images), 6. PDR (116 images) and 7. Advanced PDR (145 images).
If you use the dataset, please cite the paper:
V. E. Castillo Benítez, I. Castro Matto, J. C. Mello Román, J. L. Vázquez Noguera, M. García-Torres, J. Ayala, D. P. Pinto-Roa, P. E. Gardel-Sotomayor, J. Facon, and S. A. Grillo, Dataset from fundus images for the study of diabetic retinopathy, Data in Brief, vol. 36, p. 107068, Jun. 2021. doi: https://doi.org/10.1016/j.dib.2021.10706
Retinal Image Enhancement via a Multiscale Morphological Approach with OCCO Filter
No full textRetinal images are widely used for diagnosis and eye disease detection. However, due to the acquisition process, retinal images often have problems such as low contrast, blurry details or artifacts. These problems may severely affect the diagnosis. Therefore, it is very impor tant to enhance the visual quality of such images. Contrast enhancement is a pre-processing applied to images to improve their visual quality. This technique betters the identification of retinal structures in degraded reti nal images. In this work, a novel algorithm based on multi-scale mathe matical morphology is presented. First, the original image is blurred us ing the Open-Close Close-Open (OCCO) filter to reduce any artifacts in the image. Next, multiple bright and dark features are extracted from the filtered image by the Top-Hat transform. Finally, the maximum bright values are added to the original image and the maximum dark values are subtracted from the original image, previously adjusted by a weight. The algorithm was tested on 397 retinal images from the public STARE database. The proposed algorithm was compared with state of the art al gorithms and results show that the proposal is more efficient in improving contrast, maintaining similarity with the original image and introducing less distortion than the other algorithms. According to ophthalmologists, the algorithm, by improving retinal images, provides greater clarity in the blood vessels of the retina and would facilitate the identification of pathologies.Consejo Nacional de Ciencia y TecnologíaPROCIENCI
Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images with Residual Neural Networks.
No full textOcular toxoplasmosis (OT) is commonly diagnosed through the analysis of fundus images of the eye by a specialist. Despite Deep Learning being widely used to process and recognize pathologies in medical images, the diagnosis of ocular toxoplasmosis(OT) has not yet received much attention. A predictive computational model is a valuable time-saving option if used as a support tool for the diagnosis of OT. It could also help diagnose atypical cases, being particularly useful for ophthalmologists who have less experience. In this work, we propose the use of a deep learning model to perform automatic diagnosis of ocular toxoplasmosis from images of the eye fundus. A pretrained residual neural network is fine-tuned on a dataset of samples collected at the medical center of Hospital de Clínicas in Asunción, Paraguay. With sensitivity and specificity rates equal to 94% and 93%,respectively, the results show that the proposed model is highly promising. In order to replicate the results and advance further in this area of research, an open data set of images of the eye fundus labeled by ophthalmologists is made available.CONACYT - Consejo Nacional de Ciencia y TecnologíaPROCIENCI
A Trust-Based Methodology to Evaluate Deep Learning Models for Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images
No full textIn the automatic diagnosis of ocular toxoplasmosis (OT), Deep Learning (DL) has arisen as a powerful and promising approach for diagnosis. However, despite the good performance of the models, decision rules should be interpretable to elicit trust from the medical community. Therefore, the development of an evaluation methodology to assess DL models based on interpretability methods is a challenging task that is necessary to extend the use of AI among clinicians. In this work, we propose a novel methodology to quantify the similarity between the decision rules used by a DL model and an ophthalmologist, based on the assumption that doctors are more likely to trust a prediction that was based on decision rules they can understand. Given an eye fundus image with OT, the proposed methodology compares the segmentation mask of OT lesions labeled by an ophthalmologist with the attribution matrix produced by interpretability methods. Furthermore, an open dataset that includes the eye fundus images and the segmentation masks is shared with the community. The proposal was tested on three different DL architectures. The results suggest that complex models tend to perform worse in terms of likelihood to be trusted while achieving better results in sensitivity and specificity
Dataset of fundus images for the diagnosis of ocular toxoplasmosis
No full textToxoplasmosis chorioretinitis is commonly diagnosed by an ophthalmologist through the evaluation of the fundus images of a patient. Early detection of these lesions may help to prevent blindness. In this article we present a data set of fundus images labeled into three categories: healthy eye, inactive and active chorioretinitis. The dataset was developed by three ophthalmologists with expertise in toxoplasmosis detection using fundus images. The dataset will be of great use to researchers working on ophthalmic image analysis using artificial intelligence techniques for the automatic detection of toxoplasmosis chorioretinitis
Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images with Residual Neural Networks
No full textAutomatic diagnosis of diabetic retinopathy from fundus images using neuro-evolutionary algorithms
No full textAddress for correspondence: José Luis Vázquez Noguera, Universidad Americana, Brasilia 1100, Asunción, Paraguay; E-mail: [email protected] to the presence of high glucose levels, diabetes mellitus (DM) is a widespread disease that can damage blood vessels in the retina and lead to loss of the visual system. To combat this disease, called Diabetic Retinopathy (DR), retinography, using images of the fundus of the retina, is the most used method for the diagnosis of Diabetic Retinopathy. The Deep Learning (DL) area achieved high performance for the classification of retinal images and even achieved almost the same human performance in diagnostic tasks. However, the performance of DL architectures is highly dependent on the optimal configuration of the hyperparameters. In this article, we propose the use of Neuroevolutionary Algorithms to optimize the hyperparameters corresponding to the DL model for the diagnosis of DR. The results obtained prove that the proposed method outperforms the results obtained by the classical approach.Consejo Nacional de Ciencia y TecnologíaPrograma Paraguayo para el Desarrollo de la Ciencia y Tecnología. Proyectos de investigación y desarroll
Diseño, desarrollo y evaluación de una herramienta de soporte al diagnóstico automático de toxoplasmosis ocular
No full textLa toxoplasmosis ocular (TO) es causada por el Toxoplasma Gondii que es una enfermedad parasitaria que afecta a la mayor parte de la población mundial y su diagnóstico se realiza mediante el análisis de toma de imagen de fondo de ojo realizado por un médico especialista dentro del área, pudiendo ser un oftalmólogo. El objetivo de este trabajo es diseñar, desarrollar, implementar y evaluar una herramienta de soporte al diagnóstico automático de TO. El uso de esta herramienta tiene el potencial beneficio de otorgar a los médicos una posibilidad de acceder y obtener un diagnóstico automático de coriorretinitis por toxoplasmosis en niños utilizando técnicas de inteligencia artificial.Consejo Nacional de Ciencia y TecnologíaPrograma Paraguayo para el Desarrollo de la Ciencia y Tecnología. Proyectos de investigación y desarroll
