682 research outputs found

    Medinoid : computer-aided diagnosis and localization of glaucoma using deep learning

    Get PDF
    Glaucoma is a leading eye disease, causing vision loss by gradually affecting peripheral vision if left untreated. Current diagnosis of glaucoma is performed by ophthalmologists, human experts who typically need to analyze different types of medical images generated by different types of medical equipment: fundus, Retinal Nerve Fiber Layer (RNFL), Optical Coherence Tomography (OCT) disc, OCT macula, perimetry, and/or perimetry deviation. Capturing and analyzing these medical images is labor intensive and time consuming. In this paper, we present a novel approach for glaucoma diagnosis and localization, only relying on fundus images that are analyzed by making use of state-of-the-art deep learning techniques. Specifically, our approach towards glaucoma diagnosis and localization leverages Convolutional Neural Networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), respectively. We built and evaluated different predictive models using a large set of fundus images, collected and labeled by ophthalmologists at Samsung Medical Center (SMC). Our experimental results demonstrate that our most effective predictive model is able to achieve a high diagnosis accuracy of 96%, as well as a high sensitivity of 96% and a high specificity of 100% for Dataset-Optic Disc (OD), a set of center-cropped fundus images highlighting the optic disc. Furthermore, we present Medinoid, a publicly-available prototype web application for computer-aided diagnosis and localization of glaucoma, integrating our most effective predictive model in its back-end

    Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models

    Get PDF
    In this research, we propose Particle Swarm Optimization (PSO)-enhanced ensemble deep neural networks for optic disc (OD) segmentation using retinal images. An improved PSO algorithm with six search mechanisms to diversify the search process is introduced. It consists of an accelerated super-ellipse action, a refined super-ellipse operation, a modified PSO operation, a random leader-based search operation, an average leader-based search operation and a spherical random walk mechanism for swarm leader enhancement. Owing to the superior segmentation capabilities of Mask R-CNN, transfer learning with a PSO-based hyper-parameter identification method is employed to generate the fine-tuned segmenters for OD segmentation. Specifically, we optimize the learning parameters, which include the learning rate and momentum of the transfer learning process, using the proposed PSO algorithm. To overcome the bias of single networks, an ensemble segmentation model is constructed. It incorporates the results of distinctive base segmenters using a pixel-level majority voting mechanism to generate the final segmentation outcome. The proposed ensemble network is evaluated using the Messidor and Drions data sets and is found to significantly outperform other deep ensemble networks and hybrid ensemble clustering models that are incorporated with both the original and state-of-the-art PSO variants. Additionally, the proposed method statistically outperforms existing studies on OD segmentation and other search methods for solving diverse unimodal and multimodal benchmark optimization functions and the detection of Diabetic Macular Edema

    Retinal image quality assessment using deep convolutional neural networks

    Get PDF
    Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)Diabetic Retinopathy (DR) and diabetic macular edema (DME) are the damages caused to the retina and are complications that can affect the diabetic population. Diabetic retinopathy (DR), is the most common disease due to the presence of exudates and has three levels of severity, such as mild, moderate and severe, depending on the exudates distribution in the retina. For screening of diabetic retinopathy or a population-based clinical study, a large number of digital fundus images are captured and to be possible to recognize the signs of DR and DME, it is necessary that the images have quality, because low-quality images may force the patient to return for a second examination, wasting time and possibly delaying treatment. These images are evaluated by trained human experts, which can be a time-consuming and expensive task due to the number of images that need to be examined. Therefore, this is a field that would be hugely benefited with the development of an automated eye fundus quality assessment and analysis systems. It can potentially facilitate health care in remote regions and in developing countries where reading skills are scarce. Deep Learning is a kind of Machine Learning method that involves learning multi-level representations that begin with raw data entry and gradually moves to more abstract levels through non-linear transformations. With enough training data and sufficiently deep architectures, neural networks, such as Convolutional Neural Networks (CNN), can learn very complex functions and discover complex structures in the data. Thus, Deep Learning emerges as a powerful tool for medical image analysis and evaluation of retinal image quality using computer-aided diagnosis. Therefore, the aim of this study is to automatically assess all the three quality parameters alone (focus, illumination and color), and then an overall quality of fundus images assessment, classifying the images into the classes “accept” or “reject with a Deep Learning approach using convolutional neural networks (CNN). For the overall classification, the following results were obtained: test accuracy=97.89%, SN=97.9%, AUC=0.98 and 1-score=97.91%.A retinopatia diabética (RD) e o edema macular diabético (EMD) são patologias da retina e são uma complicação que pode afetar a população diabética. A retinopatia diabética é a doença mais comum devido à presença de exsudatos e possui três níveis de gravidade, como leve, moderado e grave, dependendo da distribuição dos exsudatos na retina. Para triagem da retinopatia diabética ou estudo clínico de base populacional, um grande número de imagens digitais de fundo do olho são capturadas e para ser possível reconhecer os sinais da RD e EMD, é necessário que as imagens tenham qualidade, pois imagens de baixa qualidade podem forçar o paciente a retornar para um segundo exame, perdendo tempo e, possivelmente, retardando o tratamento. Essas imagens são avaliadas por especialistas humanos treinados, o que pode ser uma tarefa demorada e cara devido ao número de imagens que precisam de ser examinadas. Portanto, este é um campo que seria enormemente beneficiado com o desenvolvimento de sistemas automatizados de avaliação e análise da qualidade da imagem do fundo de olho. Pode potencialmente facilitar a assistência médica em regiões remotas e em países em desenvolvimento, onde as habilidades de leitura são escassas. Deep Learning é um tipo de método de Machine Learning que envolve a aprendizagem de representações em vários níveis que começam com a entrada de dados brutos e gradualmente se transformam para níveis mais abstratos através de transformações não lineares, para se obterem as previsões. Com dados de treino suficientes e arquiteturas suficientemente profundas, as redes neuronais, como as Convolutional Neural Networks (CNN), podem aprender funções muito complexas e descobrir estruturas complexas nos dados. Assim, o Deep Learning surge como uma ferramenta poderosa para analisar imagens médicas para avaliação da qualidade da retina, usando diagnóstico auxiliado por computador a partir do fundo do olho. Portanto, o objetivo deste estudo é avaliar automaticamente a qualidade geral das imagens do fundo, classificando as imagens em “aceites” ou “rejeitadas”, com base em três parâmetros principais, como o foco, a iluminação e cor com abordagem de Deep Learning usando convolutional neural networks (CNN). Para a classificação geral da qualidade das imagens, obtiveram-se os seguintes resultados: acurácia do teste = 97,89%, SN = 97,9%, AUC = 0,98 e 1-score=97.91%
    corecore