An effective deep learning network for detecting and classifying glaucomatous eye

Glaucoma is a well-known complex disease of the optic nerve that gradually damages eyesight due to the increase of intraocular pressure inside the eyes. Among two types of glaucoma, open-angle glaucoma is mostly happened by high intraocular pressure and can damage the eyes temporarily or sometimes permanently, another one is angle-closure glaucoma. Therefore, being diagnosed in the early stage is necessary to safe our vision. There are several ways to detect glaucomatous eyes like tonometry, perimetry, and gonioscopy but require time and expertise. Using deep learning approaches could be a better solution. This study focused on the recognition of open-angle affected eyes from the fundus images using deep learning techniques. The study evolved by applying VGG16, VGG19, and ResNet50 deep neural network architectures for classifying glaucoma positive and negative eyes. The experiment was executed on a public dataset collected from Kaggle; however, every model performed better after augmenting the dataset, and the accuracy was between 93% and 97.56%. Among the three models, VGG19 achieved the highest accuracy at 97.56%

Artificial Intelligence Techniques in Medical Imaging: A Systematic Review

This scientific review presents a comprehensive overview of medical imaging modalities and their diverse applications in artificial intelligence (AI)-based disease classification and segmentation. The paper begins by explaining the fundamental concepts of AI, machine learning (ML), and deep learning (DL). It provides a summary of their different types to establish a solid foundation for the subsequent analysis. The prmary focus of this study is to conduct a systematic review of research articles that examine disease classification and segmentation in different anatomical regions using AI methodologies. The analysis includes a thorough examination of the results reported in each article, extracting important insights and identifying emerging trends. Moreover, the paper critically discusses the challenges encountered during these studies, including issues related to data availability and quality, model generalization, and interpretability. The aim is to provide guidance for optimizing technique selection. The analysis highlights the prominence of hybrid approaches, which seamlessly integrate ML and DL techniques, in achieving effective and relevant results across various disease types. The promising potential of these hybrid models opens up new opportunities for future research in the field of medical diagnosis. Additionally, addressing the challenges posed by the limited availability of annotated medical images through the incorporation of medical image synthesis and transfer learning techniques is identified as a crucial focus for future research efforts

Image preprocessing in classification and identification of diabetic eye diseases

Diabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity. © 2021, The Author(s)

Qhaway: una herramienta de apoyo para el diagnóstico del glaucoma con aprendizaje profundo

Propone un método para el diagnóstico del glaucoma basado en un modelo híbrido de modelos DL, con el cual usando imágenes del fondo de ojo de un paciente se consigue hacer el diagnóstico con alta precisión. Se consideró la integración de los dataset públicos de glaucoma HRF, Drishti-GS1, sjchoi86-HRF, RIM-ONE y ACRIMA, con un total de 1707 imágenes (919 normal y 788 glaucoma) del fondo de ojo, un modelo híbrido de Voting sobre los modelos de DL ResNet50 con dos tipos de fine tuning y ResNet50V2, y la implementación usando Keras y Tensor Flow, con lo que se consiguió un diagnóstico con exactitud del 96.55%, sensibilidad del 98.54% y especificidad del 94.32%. Además, los experimentos numéricos muestran que el aprendizaje usando 5 bases de datos permite mejores resultados que por separado, incluso aplicando transfer learning, también muestran que el modelo híbrido voting genera una exactitud superior en 20.69% a la mejor exactitud obtenido por el mejor modelo de DL (DenseNet169) usando un dataset, 13.22% al mejor modelo (ResNet50V2) usando transfer learning con los 5 datasets, y 1.72% al mejor modelo (ResNet50) considerando los 5 dataset

Machine Learning Techniques, Detection and Prediction of Glaucoma– A Systematic Review

Globally, glaucoma is the most common factor in both permanent blindness and impairment. However, the majority of patients are unaware they have the condition, and clinical practise continues to face difficulties in detecting glaucoma progression using current technology. An expert ophthalmologist examines the retinal portion of the eye to see how the glaucoma is progressing. This method is quite time-consuming, and doing it manually takes more time. Therefore, using deep learning and machine learning techniques, this problem can be resolved by automatically diagnosing glaucoma. This systematic review involved a comprehensive analysis of various automated glaucoma prediction and detection techniques. More than 100 articles on Machine learning (ML) techniques with understandable graph and tabular column are reviewed considering summery, method, objective, performance, advantages and disadvantages. In the ML techniques such as support vector machine (SVM), and K-means. Fuzzy c-means clustering algorithm are widely used in glaucoma detection and prediction. Through the systematic review, the most accurate technique to detect and predict glaucoma can be determined which can be utilized for future betterment

Aprendizaje profundo con predicción monocular de profundidad estéreo para el diagnóstico de glaucoma

[Resumen] El diagnóstico de afecciones oculares se realiza mediante el apoyo de diferentes modalidades de imágenes oftalmológicas. En concreto, en el diagnóstico de glaucoma, grave enfermedad que apenas presenta síntomas en sus etapas iniciales, es útil la utilización de retinografías para obtener indicadores de la presencia de la enfermedad. En general, las retinografías están ampliamente extendidas en el mundo de la oftalmología por ser de obtención no-invasiva. Sin embargo, existen diferentes tipos de retinografías. Descatan las retinografías monoculares, de fácil obtención, y las estereográficas, más complejas de obtener y menos accesibles en el ámbito hospitalario por necesidad de cámaras especializadas, pero que arrojan información sobre la profundidad del fondo ocular muy relevante a la hora de diagnósticar el glaucoma. En este proyecto se plantea la utilización de metodologías de aprendizaje profundo mediante diferentes aproximaciones para la obtención de la información de profundidad de las retinografías estereográficas pero a partir de retinografías monoculares. Por un lado, se plantea la predicción directa del mapa de profundidad del fondo ocular. Por otro lado, se plantea la predicción del par estereográfico a partir de la otra imagen del par, considerada una retinografía monocular. Finalmente, una vez obtenida esta información de profundidad, se plantea el uso del conocimiento adquirido en esta tarea de pre-entrenamiento para entrenar otro modelo cuyo objetivo sea realizar la tarea de segmentación semántica de disco y copa, regiones del fondo del ojo cuyo ratio es indicador de la presencia de glaucoma. Este procedimiento es conocido como transfer-learning, del cual se quiere demostrar su validez en tareas contenidas dentro del mismo campo semántico. Al comparar los resultados obtenidos por los modelos de segmentación utilizando el conocimiento transferido de las otras tareas con modelos de segmentación entrenados sin este conocimiento, se ha podido demostrar que el transfer-learning mejora los resultados en cuanto a la segmentación de la copa se refiere. En el caso de la segmentación de disco, los resultados se mantienen parejos a los de los modelos de referencia. Por otro lado, se han encontrado indicios de que dependiendo de la naturaleza del problema a resolver, es posible que, si la tarea de pre-entrenamiento aprende detalles demasiado específicos sobre su propia tarea, el conocimiento transferido empeore los resultados de la segmentación, como es el caso de la tarea de predicción del mapa de profundidad. No obstante, la tarea de predicción del par estereográfico muestra una tendencia a mejorar la segmentación cuanto mejor resuelva su tarea el modelo de pre-entrenamiento. Estas posibilidades plantean nuevas líneas de investigación que sería interesante seguir en un futuro.[Abstract] The diagnosis of ocular conditions is made through the support of different ophthalmological imaging modalities. In particular, in the diagnosis of glaucoma, a serious disease that has nearly no symptoms on its early stages, the use of retinographies is useful in order to obtain indicators of the presence of the disease. In general, retinographies are broadly used on the ophthalmological world because they can be obtained in a non-invasive way. However, there are different types of retinographies, standing out the monocular ones, easily obtained, and the stereographic ones, more complex to obtain and less accesible in the hospital setting due to the need for specialized cameras, but which give information about the depth of the ocular fundus, which is very relevant when diagnosing glaucoma. In this project, the use of deep learning methodologies is proposed through different approaches to obtain the depth information derived from the stereographic retinographies but from monocular retinographies. On one hand, direct prediction of the ocular fundus depth map is proposed. On the other hand, the prediction of the stereographic pair from the other image of the pair is considered. Finally, once the depth information has been obtained, the use of the knowledge acquired during this pre-training tasks is used to train another model whose objective is to perform the task of cup and disc semantic segmentation, regions of the ocular fundus whose ratio is an indicator of the presence of glaucoma. This procedure is known as transfer-learning and the final objetive of the project is to show its validity in the tasks contained in the same semantic field. When comparing the results obtained by the segmentation models using the transferred knowledge from the other tasks with the segmentation models trained without this knowledge, it has been shown that transfer-learning improved the results of the cup segmentation. In the case of disk segmentation, the results do not improve nor get worse compared with the reference ones. On the other hand, indications have been found that depending on the nature of the problem to be solved, it can be that, if the pre-training task learns too specific details about his own task, the transferred knowledge can make the segmentation results worse, which is the case with the depth map prediction task. Nevertheless, the stereo par prediction task shows a tendency to improve the segmentation the better the pre-training model solves its task. These possibilities raise new lines of research that it would be interesting to pursue in the future.Traballo fin de grao. Enxeñaría Informática. Curso 2020/202