Evolving Convolutional Neural Networks for Glaucoma Diagnosis / Redes neurais convolucionais em evolução para diagnóstico de glaucoma

O glaucoma é uma doença ocular que causa danos ao nervo óptico do olho e sucessivo estreitamento do campo visual nos pacientes afetados, o que pode levar o paciente, em estágio avançado, à cegueira. Este trabalho apresenta um estudo sobre o uso de Redes Neurais Convolucionais (CNNs) para o diagnóstico automático através de imagens de fundo de olho. No entanto, a construção de uma CNN capaz de alcançar resultados satisfatórios para o diagnóstico do glaucoma, envolve muito esforço que, em muitas situações, nem sempre é capaz de tais resultados. O objetivo deste trabalho é utilizar um algoritmo genético (AG) para otimizar arquiteturas de CNNs através da técnica de evolução de algoritmos que possa aprimorar o diagnóstico do glaucoma em imagens de fundo do olho do conjunto de dados RIM-ONE-r2. Nosso artigo demonstra resultados satisfatórios após o treinamento do melhor indivíduo escolhido pelo AG, com a obtenção de uma acurácia de 91%

The potential application of artificial intelligence for diagnosis and management of glaucoma in adults

BACKGROUND: Glaucoma is the most frequent cause of irreversible blindness worldwide. There is no cure, but early detection and treatment can slow the progression and prevent loss of vision. It has been suggested that artificial intelligence (AI) has potential application for detection and management of glaucoma. SOURCES OF DATA: This literature review is based on articles published in peer-reviewed journals. AREAS OF AGREEMENT: There have been significant advances in both AI and imaging techniques that are able to identify the early signs of glaucomatous damage. Machine and deep learning algorithms show capabilities equivalent to human experts, if not superior. AREAS OF CONTROVERSY: Concerns that the increased reliance on AI may lead to deskilling of clinicians. GROWING POINTS: AI has potential to be used in virtual review clinics, telemedicine and as a training tool for junior doctors. Unsupervised AI techniques offer the potential of uncovering currently unrecognized patterns of disease. If this promise is fulfilled, AI may then be of use in challenging cases or where a second opinion is desirable. AREAS TIMELY FOR DEVELOPING RESEARCH: There is a need to determine the external validity of deep learning algorithms and to better understand how the 'black box' paradigm reaches results

Improving glaucoma diagnosis assembling deep networks and voting schemes

Glaucoma is a group of eye conditions that damage the optic nerve, the health of which is vital for good eyesight. This damage is often caused by higher-than-normal pressure in the eye. In the past few years, the applications of artificial intelligence and data science have increased rapidly in medicine especially in imaging applications. In particular, deep learning tools have been successfully applied obtaining, in some cases, results superior to those obtained by humans. In this article, we present a soft novel ensemble model based on the K-NN algorithm, that combines the probability of class membership obtained by several deep learning models. In this research, three models of different nature (CNN, CapsNets and Convolutional Autoencoders) have been selected searching for diversity. The latent space of these models are combined using the local information provided by the true sample labels and the K-NN algorithm is applied to determine the final decision. The results obtained on two different datasets of retinal images show that the proposed ensemble model improves the diagnosis capabilities for both the individual models and the state-of-the-art results.This research was funded by Instituto de Salud Carlos III grant number AES2017-PI17/007 and Fundación Séneca grant number 20901/PI/18. The APC was funded by Fundación Séneca grant number 20901/PI/18