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Title: :  Robust Glaucoma Prediction from Fundus Images using DenseNet201 &NASNetMobile
PaperId: :  26387
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 11 Issue 2 2025
DUI:    16.0415/IJARIIE-26387
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
A.GeethanjaliSri Venkatesa Perumal college of Engineering and Technology
M.SathwikSri Venkatesa Perumal college of Engineering and Technology
K.Indrasena ReddySri Venkatesa Perumal college of Engineering and Technology
M.RamyaSri Venkatesa Perumal college of Engineering and Technology
N.Lakshmi PathiSri Venkatesa Perumal college of Engineering and Technology
M.BobbySri Venkatesa Perumal college of Engineering and Technology

Abstract

Electronics and Communication Engineering
cup-to-disc ratio (CDR), optic nerve head (ONH), optic disc (OD), optic cup (OC),neuro retinal rim(NRR)
Glaucoma is a progressive eye disease affecting approximately 64 million people globally, leading to damage of the optic nerve head (ONH) and potential irreversible blindness. Early detection is critical to prevent vision loss; however, traditional clinical approaches, such as manual segmentation of the optic cup and disc for cup-to-disc ratio (CDR) calculation, are often time-consuming, subjective, and dependent on expert evaluation. To overcome these limitations, this study proposes an automated glaucoma detection system based on deep learning, utilizing retinal fundus images for binary classification of healthy and glaucomatous eyes. The system employs two advanced convolutional neural networks, DenseNet201 and NASNetMobile, both trained using transfer learning techniques and fine-tuned for optimal performance. Preprocessing techniques such as histogram equalization are applied to enhance image contrast, and class imbalance is managed using computed class weights. The model focuses on extracting key features from the optic disc (OD) and optic cup (OC) areas, emphasizing crucial indicators like the cup-to-disc ratio (CDR) and the neuroretinal rim (NRR) structure. Performance evaluation is conducted by comparing the classification accuracy, precision, and recall of both models to identify the more effective solution for practical glaucoma screening. DenseNet201, recognized for its deep feature extraction capabilities, and NASNetMobile, optimized for lightweight deployment, offer valuable insights into achieving a balance between accuracy and computational efficiency. This research highlights the promising role of deep learning in supporting clinicians with faster, more objective, and reliable glaucoma diagnosis.

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IJARIIE A.Geethanjali, M.Sathwik, K.Indrasena Reddy, M.Ramya, N.Lakshmi Pathi, and M.Bobby. "Robust Glaucoma Prediction from Fundus Images using DenseNet201 &NASNetMobile" International Journal Of Advance Research And Innovative Ideas In Education Volume 11 Issue 2 2025 Page 3072-3079
MLA A.Geethanjali, M.Sathwik, K.Indrasena Reddy, M.Ramya, N.Lakshmi Pathi, and M.Bobby. "Robust Glaucoma Prediction from Fundus Images using DenseNet201 &NASNetMobile." International Journal Of Advance Research And Innovative Ideas In Education 11.2(2025) : 3072-3079.
APA A.Geethanjali, M.Sathwik, K.Indrasena Reddy, M.Ramya, N.Lakshmi Pathi, & M.Bobby. (2025). Robust Glaucoma Prediction from Fundus Images using DenseNet201 &NASNetMobile. International Journal Of Advance Research And Innovative Ideas In Education, 11(2), 3072-3079.
Chicago A.Geethanjali, M.Sathwik, K.Indrasena Reddy, M.Ramya, N.Lakshmi Pathi, and M.Bobby. "Robust Glaucoma Prediction from Fundus Images using DenseNet201 &NASNetMobile." International Journal Of Advance Research And Innovative Ideas In Education 11, no. 2 (2025) : 3072-3079.
Oxford A.Geethanjali, M.Sathwik, K.Indrasena Reddy, M.Ramya, N.Lakshmi Pathi, and M.Bobby. 'Robust Glaucoma Prediction from Fundus Images using DenseNet201 &NASNetMobile', International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, 2025, p. 3072-3079. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Robust__Glaucoma_Prediction_from__Fundus__Images_using__DenseNet201__NASNetMobile_ijariie26387.pdf (Accessed : ).
Harvard A.Geethanjali, M.Sathwik, K.Indrasena Reddy, M.Ramya, N.Lakshmi Pathi, and M.Bobby. (2025) 'Robust Glaucoma Prediction from Fundus Images using DenseNet201 &NASNetMobile', International Journal Of Advance Research And Innovative Ideas In Education, 11(2), pp. 3072-3079IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Robust__Glaucoma_Prediction_from__Fundus__Images_using__DenseNet201__NASNetMobile_ijariie26387.pdf (Accessed : )
IEEE A.Geethanjali, M.Sathwik, K.Indrasena Reddy, M.Ramya, N.Lakshmi Pathi, and M.Bobby, "Robust Glaucoma Prediction from Fundus Images using DenseNet201 &NASNetMobile," International Journal Of Advance Research And Innovative Ideas In Education, vol. 11, no. 2, pp. 3072-3079, Mar-App 2025. [Online]. Available: https://ijariie.com/AdminUploadPdf/Robust__Glaucoma_Prediction_from__Fundus__Images_using__DenseNet201__NASNetMobile_ijariie26387.pdf [Accessed : ].
Turabian A.Geethanjali, M.Sathwik, K.Indrasena Reddy, M.Ramya, N.Lakshmi Pathi, and M.Bobby. "Robust Glaucoma Prediction from Fundus Images using DenseNet201 &NASNetMobile." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 11 number 2 ().
Vancouver A.Geethanjali, M.Sathwik, K.Indrasena Reddy, M.Ramya, N.Lakshmi Pathi, and M.Bobby. Robust Glaucoma Prediction from Fundus Images using DenseNet201 &NASNetMobile. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2025 [Cited : ]; 11(2) : 3072-3079. Available from: https://ijariie.com/AdminUploadPdf/Robust__Glaucoma_Prediction_from__Fundus__Images_using__DenseNet201__NASNetMobile_ijariie26387.pdf
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