[{“box”:0,”content”:”[if 992 equals=”Open Access”]n
n
Open Access
nn
n
n[/if 992]n
n
n
n
n
n
Tejashwini N, Shantakumar B Patil, Rabbani S, Shreyas T.S, Sumanth V, Darshan R,
n
- n t
n
n
n[/foreach]
n
n[if 2099 not_equal=”Yes”]n
- [foreach 286] [if 1175 not_equal=””]n t
- Associate Professor, HOD Professor, Student, Student, Student, Student Department. of Computer Science, Sai Vidya Institute of Technology, Department. of Computer Science, Sai Vidya Institute of Technology, Department. of Computer Science, Sai Vidya Institute of Technology, Department. of Computer Science, Sai Vidya Institute of Technology, Department. of Computer Science, Sai Vidya Institute of Technology, Department. of Computer Science, Sai Vidya Institute of Technology Bengaluru, Bengaluru, Bengaluru, Bengaluru, Bengaluru, Bengaluru India, India, India, India, India, India
n[/if 1175][/foreach]
n[/if 2099][if 2099 equals=”Yes”][/if 2099]n
Abstract
nThis project delves into the evaluation of three prominent deep learning architectures Basic CNN, ResNet, and DenseNet for their efficacy in detecting diabetic retinopathy from retinal images. Utilizing a diverse dataset, the study employs standard deep learning frameworks to train and validate each model. The focus extends to exploring the potential benefits of transfer learning on a limited dataset. Evaluation metrics like specificity, sensitivity, and accuracy are employed for a comprehensive a comparison study of the models. The results and ensuing discussion reveal nuanced performance characteristics, highlighting potential tradeoffs between accuracy and computational demands. This research contributes valuable insights to the field of medical imaging and analysis, aiding in the selection of optimal models for the precise detection of diabetic retinopathy. The findings are highly significant for healthcare professionals and researchers as they provide valuable insights that can guide future efforts in enhancing and refining deep learning models specifically designed for the diagnosis of diabetic retinopathy. This advancement has the potential to improve early detection, treatment, and overall management of the condition, ultimately benefiting patients and contributing to the field of medical research
n
Keywords: Diabetic retinopathy, Fundus images, Convolutional Neural Architecture. sensitivity, accuracy, medical imaging, model evaluation, computational demands, healthcare, early detection, treatment, medical research
n[if 424 equals=”Regular Issue”][This article belongs to Research & Reviews : A Journal of Medical Science and Technology(rrjomst)]
n
n
n
n
n
nn[if 992 equals=”Open Access”] Full Text PDF Download[/if 992] n
nn[if 379 not_equal=””]n
Browse Figures
n
n[/if 379]n
References
n[if 1104 equals=””]n
- GT, Andreao. RV, Dorizz. B and Teatini Salles. EO, “Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Computers in Biology and Medicine”, 116, 103537, 2020.
- H, Yang. K, Gao. M, Zhang. D, Ma. H and Qian. W, “An interpretable ensemble deep learning model for diabetic retinopathy disease classification”, 41st Annual International conference of the IEEE engineering in medicine and biology society (EMBC), pp. 2045–2048, 2019.
- H, Coenen. F, Broadbent. DM, Harding. SP and Zheng. Y, “Convolutional neural networks for diabetic retinopathy”, Procedia Comput Sci., 90:200–5, 2016.
- C. H, Huynh-The. T and Lee. S, “Retinal vessel segmentation using round-wise features aggregation on bracket-shaped convolutional neural networks”, Proceedings of the annual International Conference of the IEEE Engineering in Medicine and biology society, EMBS, p. 36–9, 2019.
- Alyoubi WL, Shalash WM, Abulkhair MF. Diabetic retinopathy detection through deep learning techniques: A review. Informatics in medicine unlocked. 2020 Jan 1;20:100377–7. Available from: https://www.sciencedirect.com/science/article/pii/S2352914820302069
- Gao Z, Li J, Guo J, Chen Y, Yi Z, Zhong J. Diagnosis of diabetic retinopathy using deep neural networks. IEEE Access. 2018 Dec 19;7:3360-70.
- Suchetha Manikandan, Raman R, Ramachandran Rajalakshmi, S Tamilselvi, R Janani Surya. Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis. Indian Journal of Ophthalmology/Indian journal of ophthalmology. 2023 May 1;71(5):1783–96. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10391382/
- H. Kim and S. W. Choi, “A Deep Learning-Based Approach for Radiation Pattern Synthesis of an Array Antenna,” in IEEE Access, vol. 8, pp. 226059-226063, 2020, doi: 10.1109/ACCESS.2020.3045464.
- Niu Y, Gu L, Zhao Y, Lu F. Explainable Diabetic Retinopathy Detection and Retinal Image Generation. IEEE Journal of Biomedical and Health Informatics. 2022 Jan ;26(1):44–55. Available from: https://arxiv.org/pdf/2107.00296v1.pdf
- Li Y, Zhang Y, Cui W, Lei B, Kuang X, Zhang T. Dual encoder-based dynamic-channel graph convolutional network with edge enhancement for retinal vessel segmentation. IEEE Transactions on Medical Imaging. 2022 Feb 15;41(8):1975-89.
nn[/if 1104][if 1104 not_equal=””]n
- [foreach 1102]n t
- [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
n[/foreach]
n[/if 1104]
nn
nn[if 1114 equals=”Yes”]n
n[/if 1114]
n
n
n
Research & Reviews : A Journal of Medical Science and Technology
n
n
n
n
n
n
| Volume | 13 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | May 1, 2024 | |
| Accepted | June 24, 2024 | |
| Published | August 13, 2024 |
n
n
n
n
n
n nfunction myFunction2() {nvar x = document.getElementById(“browsefigure”);nif (x.style.display === “block”) {nx.style.display = “none”;n}nelse { x.style.display = “Block”; }n}ndocument.querySelector(“.prevBtn”).addEventListener(“click”, () => {nchangeSlides(-1);n});ndocument.querySelector(“.nextBtn”).addEventListener(“click”, () => {nchangeSlides(1);n});nvar slideIndex = 1;nshowSlides(slideIndex);nfunction changeSlides(n) {nshowSlides((slideIndex += n));n}nfunction currentSlide(n) {nshowSlides((slideIndex = n));n}nfunction showSlides(n) {nvar i;nvar slides = document.getElementsByClassName(“Slide”);nvar dots = document.getElementsByClassName(“Navdot”);nif (n > slides.length) { slideIndex = 1; }nif (n (item.style.display = “none”));nArray.from(dots).forEach(nitem => (item.className = item.className.replace(” selected”, “”))n);nslides[slideIndex – 1].style.display = “block”;ndots[slideIndex – 1].className += ” selected”;n}n”}]