Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES

MethodsX Pub Date : 2025-01-25 DOI:10.1016/j.mex.2025.103182

Ranjana Agrawal , Sucheta Kulkarni , Madan Deshpande , Anita Gaikwad , Rahee Walambe , Ketan V. Kotecha

{"title":"Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier","authors":"Ranjana Agrawal , Sucheta Kulkarni , Madan Deshpande , Anita Gaikwad , Rahee Walambe , Ketan V. Kotecha","doi":"10.1016/j.mex.2025.103182","DOIUrl":null,"url":null,"abstract":"<div><div>Retinopathy of prematurity (ROP) is a retinal disorder that can cause blindness in premature infants with low birth weight. Early detection and timely treatment are crucial to prevent blindness associated with ROP. It's essential to identify the stage and presence of Plus disease accurately when examining retinal images of at-risk infants. We are developing an explainable automated ROP screening system for the HVDROPDB datasets. The fundus images were classified as without stage (Normal)/with Stage (ROP) by segmenting the ridge. Stages 1–3 were classified using machine Learning (ML) models.<ul><li><span>•</span><span><div>This study aims to improve accuracy of Stages 1–3 classification and identify Pre-plus/ Plus disease using MultiCNN_LSTM networks. This is accomplished by using multiple CNNs (Convolutional Neural Networks) to extract features and LSTM (Long Short-Term Memory) classifier to classify images.</div></span></li><li><span>•</span><span><div>Cropped STAGE dataset and HVDROPDB-PLUS dataset are constructed with RetCam and Neo images.</div></span></li><li><span>•</span><span><div>The proposed networks outperform individual CNNs and CNN_LSTM networks in terms of accuracy and F1 score.</div></span></li></ul></div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"14 ","pages":"Article 103182"},"PeriodicalIF":1.6000,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"MethodsX","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2215016125000305","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Retinopathy of prematurity (ROP) is a retinal disorder that can cause blindness in premature infants with low birth weight. Early detection and timely treatment are crucial to prevent blindness associated with ROP. It's essential to identify the stage and presence of Plus disease accurately when examining retinal images of at-risk infants. We are developing an explainable automated ROP screening system for the HVDROPDB datasets. The fundus images were classified as without stage (Normal)/with Stage (ROP) by segmenting the ridge. Stages 1–3 were classified using machine Learning (ML) models.

•
This study aims to improve accuracy of Stages 1–3 classification and identify Pre-plus/ Plus disease using MultiCNN_LSTM networks. This is accomplished by using multiple CNNs (Convolutional Neural Networks) to extract features and LSTM (Long Short-Term Memory) classifier to classify images.
•
Cropped STAGE dataset and HVDROPDB-PLUS dataset are constructed with RetCam and Neo images.
•
The proposed networks outperform individual CNNs and CNN_LSTM networks in terms of accuracy and F1 score.

Abstract Image