{"title":"使用 InceptionResnet-V2 和 Densenet121 检测糖尿病视网膜病变","authors":"Gangumolu Harsha Vardhan, Meda Venkata Sai Jyoshna, Pamarthi Kasi Viswanath, Shaik Zubayr, Velaga Sravanth","doi":"10.55529/jipirs.42.30.40","DOIUrl":null,"url":null,"abstract":"This project addresses the global health challenge posed by the prevalence of diabetic retinopathy (DR) by developing an efficient automated diagnostic system. The dataset, consisting of diverse high-resolution retinal images, underwent preprocessing to categorize images into No DR (0) and DR (1-4) classes. The First initial binary classification model using a Convolutional Neural Network (CNN) discriminated between healthy and diseased retinas. Subsequently, The second multi-class CNN model was designed to predict the severity of diabetic retinopathy (DR) across a spectrum from mild (1) to proliferative DR (4), enabling a fine-grained analysis for early identification of cases requiring urgent intervention. To address real-world complexities, potential noise in the dataset, including artifacts and exposure variations, was acknowledged. The CNN models were designed to exhibit resilience to these challenges, ensuring robust performance in clinical settings. Preprocessing is considered the common occurrence of image inversion in retinal imaging by incorporating anatomical features, such as macula position and notches, to correctly identify image orientation and enhance result interpretability. The proposed automated analysis system demonstrated promising results in accurately categorizing retinal images into No DR and DR, as well as assigning severity scores for diabetic retinopathy. This project contributes significantly to computer-aided diagnostics, Supplying a dependable instrument for promptly identifying and addressing cases of diabetic retinopathy.","PeriodicalId":517163,"journal":{"name":"Feb-Mar 2024","volume":"7 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Diabetic Retinopathy Detection Using InceptionResnet-V2 and Densenet121\",\"authors\":\"Gangumolu Harsha Vardhan, Meda Venkata Sai Jyoshna, Pamarthi Kasi Viswanath, Shaik Zubayr, Velaga Sravanth\",\"doi\":\"10.55529/jipirs.42.30.40\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This project addresses the global health challenge posed by the prevalence of diabetic retinopathy (DR) by developing an efficient automated diagnostic system. The dataset, consisting of diverse high-resolution retinal images, underwent preprocessing to categorize images into No DR (0) and DR (1-4) classes. The First initial binary classification model using a Convolutional Neural Network (CNN) discriminated between healthy and diseased retinas. Subsequently, The second multi-class CNN model was designed to predict the severity of diabetic retinopathy (DR) across a spectrum from mild (1) to proliferative DR (4), enabling a fine-grained analysis for early identification of cases requiring urgent intervention. To address real-world complexities, potential noise in the dataset, including artifacts and exposure variations, was acknowledged. The CNN models were designed to exhibit resilience to these challenges, ensuring robust performance in clinical settings. Preprocessing is considered the common occurrence of image inversion in retinal imaging by incorporating anatomical features, such as macula position and notches, to correctly identify image orientation and enhance result interpretability. The proposed automated analysis system demonstrated promising results in accurately categorizing retinal images into No DR and DR, as well as assigning severity scores for diabetic retinopathy. This project contributes significantly to computer-aided diagnostics, Supplying a dependable instrument for promptly identifying and addressing cases of diabetic retinopathy.\",\"PeriodicalId\":517163,\"journal\":{\"name\":\"Feb-Mar 2024\",\"volume\":\"7 5\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Feb-Mar 2024\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.55529/jipirs.42.30.40\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Feb-Mar 2024","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55529/jipirs.42.30.40","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
该项目通过开发高效的自动诊断系统,应对糖尿病视网膜病变(DR)的流行给全球健康带来的挑战。数据集由各种高分辨率视网膜图像组成,经过预处理后将图像分为无 DR(0)和 DR(1-4)两类。第一个使用卷积神经网络(CNN)的初始二元分类模型可区分健康视网膜和病变视网膜。随后,第二个多类 CNN 模型被设计用来预测糖尿病视网膜病变(DR)的严重程度,范围从轻度(1)到增殖性 DR(4),从而能够进行精细分析,及早识别需要紧急干预的病例。为了应对现实世界的复杂性,我们考虑到了数据集中可能存在的噪音,包括伪影和曝光变化。CNN 模型的设计能够应对这些挑战,确保在临床环境中发挥强大的性能。预处理被认为是视网膜成像中常见的图像反转现象,它结合了解剖学特征,如黄斑位置和切口,以正确识别图像方向并提高结果的可解释性。拟议的自动分析系统在准确地将视网膜图像分为无 DR 和有 DR 两类以及为糖尿病视网膜病变的严重程度评分方面取得了可喜的成果。该项目为计算机辅助诊断做出了重大贡献,为及时识别和处理糖尿病视网膜病变病例提供了可靠的工具。
Diabetic Retinopathy Detection Using InceptionResnet-V2 and Densenet121
This project addresses the global health challenge posed by the prevalence of diabetic retinopathy (DR) by developing an efficient automated diagnostic system. The dataset, consisting of diverse high-resolution retinal images, underwent preprocessing to categorize images into No DR (0) and DR (1-4) classes. The First initial binary classification model using a Convolutional Neural Network (CNN) discriminated between healthy and diseased retinas. Subsequently, The second multi-class CNN model was designed to predict the severity of diabetic retinopathy (DR) across a spectrum from mild (1) to proliferative DR (4), enabling a fine-grained analysis for early identification of cases requiring urgent intervention. To address real-world complexities, potential noise in the dataset, including artifacts and exposure variations, was acknowledged. The CNN models were designed to exhibit resilience to these challenges, ensuring robust performance in clinical settings. Preprocessing is considered the common occurrence of image inversion in retinal imaging by incorporating anatomical features, such as macula position and notches, to correctly identify image orientation and enhance result interpretability. The proposed automated analysis system demonstrated promising results in accurately categorizing retinal images into No DR and DR, as well as assigning severity scores for diabetic retinopathy. This project contributes significantly to computer-aided diagnostics, Supplying a dependable instrument for promptly identifying and addressing cases of diabetic retinopathy.