Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier
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 whe...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125000305 |
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| author | Ranjana Agrawal Sucheta Kulkarni Madan Deshpande Anita Gaikwad Rahee Walambe Ketan V. Kotecha |
| author_facet | Ranjana Agrawal Sucheta Kulkarni Madan Deshpande Anita Gaikwad Rahee Walambe Ketan V. Kotecha |
| author_sort | Ranjana Agrawal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-87cc80371937493295a1dd0d716f068d |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-87cc80371937493295a1dd0d716f068d2025-01-29T05:01:20ZengElsevierMethodsX2215-01612025-06-0114103182Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifierRanjana Agrawal0Sucheta Kulkarni1Madan Deshpande2Anita Gaikwad3Rahee Walambe4Ketan V. Kotecha5Dr. Vishwanath Karad MIT World Peace University, Pune, India; Corresponding author.PBMA's H. V. Desai Eye Hospital, Pune, IndiaPBMA's H. V. Desai Eye Hospital, Pune, IndiaPBMA's H. V. Desai Eye Hospital, Pune, IndiaE&TC dept,Symbiosis Institute of Technology, Associate faculty Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed) University, Pune, IndiaSymbiosis Institute of Technology, Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed) University, Pune 412 115, Maharashtra, IndiaRetinopathy 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.http://www.sciencedirect.com/science/article/pii/S2215016125000305MultiCNN_LSTM classifier for Classification of Early Stages and Pre-plus, plus disease of ROP |
| spellingShingle | Ranjana Agrawal Sucheta Kulkarni Madan Deshpande Anita Gaikwad Rahee Walambe Ketan V. Kotecha Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier MethodsX MultiCNN_LSTM classifier for Classification of Early Stages and Pre-plus, plus disease of ROP |
| title | Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier |
| title_full | Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier |
| title_fullStr | Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier |
| title_full_unstemmed | Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier |
| title_short | Classification of Stages 1,2,3 and Preplus, Plus disease of ROP using MultiCNN_LSTM classifier |
| title_sort | classification of stages 1 2 3 and preplus plus disease of rop using multicnn lstm classifier |
| topic | MultiCNN_LSTM classifier for Classification of Early Stages and Pre-plus, plus disease of ROP |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125000305 |
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