Hypertensive Retinopathy Detection in Fundus Images Using Deep Learning-Based Model - Shallow ConvNet

Background: Hypertensive Retinopathy (HR) is amongst the abnormalities occurred with high blood pressure. This high blood pressure level makes retinal arterial narrower, retinal hemorrhages and cotton wool spots more harmful. Based on what was mentioned, early detection of hypertensive retinopathy is pivotal to prevent its following disabilities and boost its treatment with more accurate methods. Material and Methods: The main objective of this study is to investigate an appropriate deep learning method for improving the automatic diagnosis of hypertensive retinopathy in its early stages. The complete data used in this study have been obtained from integration of Structured Analysis of the Retina (STARE) and The Digital Retinal Images for Vessel Extraction (DRIVE) datasets. Results: Interestingly, we reached an accuracy of 87.5 % after using the well-suited preprocessing method to integrate different images for further analysis by our designed convolutional neural network (CNN). Conclusion: This model performs well with integration of two mentioned datasets

A Comparison of Deep Learning Techniques for Glaucoma Diagnosis on Retinal Fundus Images

Glaucoma is one of the serious disorders which cause permanent vision loss if it left undetected. The primary cause of the disease is elevated intraocular pressure, impacting the optic nerve head (ONH) that originates from the optic disc. The variation in optic disc to optic cup ratio helps in early detection of the disease. Manual calculation of Cup to Disc Ratio (CDR) consumes more time and the prediction is also not accurate. Utilizing deep learning for the automatic detection of glaucoma facilitates precise and early identification, significantly enhancing the accuracy of glaucoma detection. The deep learning technique initiates the process by initially pre-processing the image to achieve data augmentation, followed by the segmentation of the optic disc and optic cup from the retinal fundus image. From the segmented Optic Disc (OD)and Optic Cup (OC) feature are selected and CDR calculated. Based on the CDR value the Glaucoma classification is performed. Various deep learning techniques like CNN, transfer learning, algorithm was proposed in early detection of glaucoma. From the comparative analysis glaucoma diagnosis, the proposed deep learning artifact Convolutional Neural Network outperform in early diagnosis of glaucoma providing accuracy of 99.3 8%

Automated fundus image quality assessment and segmentation of optic disc using convolutional neural networks

An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIM-ONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics

Brain cone beam computed tomography image analysis using ResNet50 for collateral circulation classification

Treatment of stroke patients can be effectively carried out with the help of collateral circulation performance. Collateral circulation scoring as it is now used is dependent on visual inspection, which can lead to an inter- and intra-rater discrepancy. In this study, a collateral circulation classification using the ResNet50 was analyzed by using cone beam computed tomography (CBCT) images for the ischemic stroke patient. The remarkable performance of deep learning classification helps neuroradiologists with fast image classification. A pre-trained deep network ResNet50 was applied to extract robust features and learn the structure of CBCT images in their convolutional layers. Next, the classification layer of the ResNet50 was performed into binary classification as “good” and “poor” classes. The images were divided by 80:20 for training and testing. The empirical results support the claim that the application of ResNet50 offers consistent accuracy, sensitivity, and specificity values. The performance value of the classification accuracy was 76.79%. The deep learning approach was employed to unveil how biological image analysis could generate incredibly dependable and repeatable outcomes. The experiments performed on CBCT images evidenced that the proposed ResNet50 using convolutional neural network (CNN) architecture is indeed effective in classifying collateral circulation

Metodologia Deep Features para Diagnóstico de Glaucoma / Deep Features Methodology for Glaucoma Diagnosis

O glaucoma é uma doença ocular caracterizada por neuropatia óptica e distúrbio visual que corresponde á escavação no disco óptico e á degeneração das fibras nervosas ópticas.  Geralmente e´ causado pelo aumento na pressão intra-ocular, que danifica o nervo óptico, resultando em perda gra- dual da visão.  Um tratamento eficaz é a redução e controle da pressão intra- ocular (PIO) que deve acontecer o mais precocemente possível de modo a limitar a progressão da doença. Vários trabalhos tem sido propostos para a realização do diagnóstico automático de glaucoma.   Assim, é vital o desen-volvimento de uma ferramenta computadorizada automática para diagnosticar a doença.  No entanto, existe ainda grande dificuldade de lidar com uma grande diversidade de imagens.  Em razão disso, tais métodos não são viáveis para o uso em programas de triagem.  Este trabalho propõe uma metodologia com a finalidade de detectar de uma maneira eficiente o glaucoma, que seja capaz de lidar com imagens diversas, através da extração de características usando Redes Neurais Convolucionais (CNNs). Nesta proposta foram utilizadas CNNs pré-treinadas e Redes Específicas construídas através de uma estratégia de otimização de arquitetura e de hiperparâmetros especifica para o problema. Destas redes foram extrá?das as características as quais foram utilizadas com o classificador Regressão Logística, apresentando resultados promissores na detecção do Glaucoma. Em experimentos realizados com 1090 imagens de qua- tro bases de dados foram obtidas acurácias de 86.8% e 86.3%.

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