Automatic diagnosis of multiple lesions in fundus images based on dual attention mechanism

2021 ◽  
Author(s):  
Jiamin Gong ◽  
Liufei Guo ◽  
Jiewei Jiang ◽  
Chengchao Wu ◽  
Mengjie Pei ◽  
...  
Keyword(s):  
Attention Mechanism ◽  
Fundus Images ◽  
Automatic Diagnosis ◽  
Multiple Lesions

Automatic Diagnosis of Glaucoma Using Digital Fundus Images

2011 ◽  
pp. 207-226
Author(s):  
Rajendra Acharya U. ◽  
Oliver Faust ◽  
Kuanyi Zhu ◽  
Irene Mei Xiu Tan ◽  
Boo Maggie ◽  
...  
Keyword(s):  
Fundus Images ◽  
Automatic Diagnosis

scholarly journals Detection of Microaneurysms in Fundus Images Based on an Attention Mechanism

Genes ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 817
Author(s):  
Lizong Zhang ◽  
Shuxin Feng ◽  
Guiduo Duan ◽  
Ying Li ◽  
Guisong Liu
Keyword(s):  
Detection Method ◽  
Preliminary Test ◽  
Attention Mechanism ◽  
Fundus Images ◽  
Test Results ◽  
Average Accuracy ◽  
Multiple Feature ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Microaneurysms (MAs) are the earliest detectable diabetic retinopathy (DR) lesions. Thus, the ability to automatically detect MAs is critical for the early diagnosis of DR. However, achieving the accurate and reliable detection of MAs remains a significant challenge due to the size and complexity of retinal fundus images. Therefore, this paper presents a novel MA detection method based on a deep neural network with a multilayer attention mechanism for retinal fundus images. First, a series of equalization operations are performed to improve the quality of the fundus images. Then, based on the attention mechanism, multiple feature layers with obvious target features are fused to achieve preliminary MA detection. Finally, the spatial relationships between MAs and blood vessels are utilized to perform a secondary screening of the preliminary test results to obtain the final MA detection results. We evaluated the method on the IDRiD_VOC dataset, which was collected from the open IDRiD dataset. The results show that our method effectively improves the average accuracy and sensitivity of MA detection.

scholarly journals Literature Survey on Diabetic Retinopathy Classification Using Deep Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 2259-2262
Author(s):  
Alfiya Md. Shaikh
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Medical Condition ◽  
Literature Survey ◽  
Fundus Images ◽  
Fundus Image ◽  
Automatic Diagnosis ◽  
Global Blindness

Abstract: Diabetic retinopathy (DR) is a medical condition that damages eye retinal tissues. Diabetic retinopathy leads to mild to complete blindness. It has been a leading cause of global blindness. The identification and categorization of DR take place through the segmentation of parts of the fundus image or the examination of the fundus image for the incidence of exudates, lesions, microaneurysms, and so on. This research aims to study and summarize various recent proposed techniques applied to automate the process of classification of diabetic retinopathy. In the current study, the researchers focused on the concept of classifying the DR fundus images based on their severity level. Emphasis is on studying papers that proposed models developed using transfer learning. Thus, it becomes vital to develop an automatic diagnosis system to support physicians in their work.

scholarly journals Artificial Intelligence Approach in Multiclass Diabetic Retinopathy Detection Using Convolutional Neural Network and Attention Mechanism

2021 ◽  
Vol 13 (3) ◽  
pp. 101-114
Author(s):  
Amnia Salma ◽  
Alhadi Bustamam ◽  
Anggun Yudantha ◽  
Andi Victor ◽  
Wibowo Mangunwardoyo
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Attention Mechanism ◽  
Fundus Images ◽  
The World ◽  
Long Time ◽  
Suitable Treatment ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

The number of people around the world who have diabetes is about 422 million. Diabetes seriously affects the blood vessels in the retina, a disease called diabetic retinopathy (DR). The ophthalmologist examines signs through fundus images, such microaneurysm, exudates and neovascularisation and determines the suitable treatment for patient based on the condition. Currently, doctors require a long time and professional skills to detect DR. This study aimed to implement artificial intelligence (AI) to resolve the lack of current methods. This study implemented AI for detecting and classifying DR. AI uses deep learning, such the attention mechanism algorithm and AlexNet architecture. The attention mechanism algorithm focuses on detecting the pathological area in the fundus images, and AlexNet is used to classify DR into five levels based on the pathological area. This study also compared AlexNet architecture with and without attention mechanism. We obtained 344 fundus images from the Kaggle dataset, which contains normal, mild, moderate, severe and proliferative DR. The highest accuracy in this study is up to 91% and used the attention mechanism algorithm and AlexNet architecture. The experiment shows that our proposed method can provide results that can detect the pathological areas and effectively classify DR. Keywords: Artificial intelligence, Diabetic Retinopathy, Attention Mechanism, AlexNet

scholarly journals Multiple Lesions Detection of Fundus Images Based on Convolution Neural Network Algorithm With Improved SFLA

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 97618-97631
Author(s):  
Weiping Ding ◽  
Ying Sun ◽  
Longjie Ren ◽  
Hengrong Ju ◽  
Zhihao Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Fundus Images ◽  
Multiple Lesions ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals Automatic Diagnosis of Ocular Toxoplasmosis from Fundus Images with Residual Neural Networks

2021 ◽  
Author(s):  
Rodrigo Parra ◽  
Verena Ojeda ◽  
José Luis Vázquez Noguera ◽  
Miguel García Torres ◽  
Julio César Mello Román ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Center ◽  
Open Data ◽  
Ocular Toxoplasmosis ◽  
Fundus Images ◽  
Time Saving ◽  
Data Set ◽  
Automatic Diagnosis ◽  
Support Tool ◽  
Proposed Model

Ocular 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.

scholarly journals An interpretable multiple-instance approach for the detection of referable diabetic retinopathy in fundus images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexandros Papadopoulos ◽  
Fotis Topouzis ◽  
Anastasios Delopoulos
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Classification Performance ◽  
Multiple Instance Learning ◽  
Attention Mechanism ◽  
Learning System ◽  
Successful Outcome ◽  
Fundus Images ◽  
Image Patches

AbstractDiabetic retinopathy (DR) is one of the leading causes of vision loss across the world. Yet despite its wide prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for monitoring their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automatically detect the disease in eye fundus images have been proposed as a means of facilitating access to retinopathy severity estimates for patients in remote regions or even for complementing the human expert’s diagnosis. Here we propose a machine learning system for the detection of referable diabetic retinopathy in fundus images, which is based on the paradigm of multiple-instance learning. Our method extracts local information independently from multiple rectangular image patches and combines it efficiently through an attention mechanism that focuses on the abnormal regions of the eye (i.e. those that contain DR-induced lesions), thus resulting in a final image representation that is suitable for classification. Furthermore, by leveraging the attention mechanism our algorithm can seamlessly produce informative heatmaps that highlight the regions where the lesions are located. We evaluate our approach on the publicly available Kaggle, Messidor-2 and IDRiD retinal image datasets, in which it exhibits near state-of-the-art classification performance (AUC of 0.961 in Kaggle and 0.976 in Messidor-2), while also producing valid lesion heatmaps (AUPRC of 0.869 in the 81 images of IDRiD that contain pixel-level lesion annotations). Our results suggest that the proposed approach provides an efficient and interpretable solution against the problem of automated diabetic retinopathy grading.

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