A novel approach to macular edema detection: DeepLabv3+ segmentation and VGG with vision transformer classification

References

• Lovic D, Piperidou A, Zografou I, et al. The growing epidemic of diabetes mellitus. Curr Vasc Pharmacol 2020;18(2):104–109.
   PubMed Web of Science ®Google Scholar
• Lahmiri S. Hybrid deep learning convolutional neural networks and optimal nonlinear support vector machine to detect presence of hemorrhage in retina. Biomed Signal Process Control. 2020;60(101978):101978.
   Google Scholar
• Foo VHX, Gupta P, Nguyen QD, et al. Decrease in choroidal vascularity index of Haller’s layer in diabetic eyes precedes retinopathy. BMJ Open Diabetes Res Care. 2020;8(1):e001295.
   PubMed Web of Science ®Google Scholar
• Wang Z, Keane PA, Chiang M, et al. Artificial intelligence and deep learning in ophthalmology. In: Artificial intelligence in medicine. Cham: Springer International Publishing; 2021. p. 1–34.
   Google Scholar
• Bhardwaj C, Jain S, Sood M. Deep learning-based diabetic retinopathy severity grading system employing quadrant ensemble model. J Digit Imaging. 2021;34(2):440–457.
   PubMed Web of Science ®Google Scholar
• Li F, Wang Y, Xu T, et al. Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs. Eye. 2022;36(7):1433–1441.
   PubMed Web of Science ®Google Scholar
• Sitaula C, Hossain MB. Attention-based VGG-16 model for COVID-19 chest X-ray image classification. Appl Intell. 2021;51(5):2850–2863.
   PubMed Web of Science ®Google Scholar
• Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv [cs.CV]. 2014.
   Google Scholar
• Chen L-C, Papandreou G, Kokkinos I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 2018;40(4):834–848.
   PubMed Web of Science ®Google Scholar
• Hu J, Chen Y, Yi Z. Automated segmentation of macular edema in OCT using deep neural networks. Med Image Anal 2019;55:216–227.
   PubMed Web of Science ®Google Scholar
• Lu D, Heisler M, Lee S, et al. Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network. Med Image Anal 2019;54:100–110.
   PubMed Web of Science ®Google Scholar
• Soomro TA, Afifi AJ, Zheng L, et al. Deep learning models for retinal blood vessels segmentation: A review. IEEE Access. 2019;7:71696–71717.
   Web of Science ®Google Scholar
• Thulkar D, Daruwala R, Sardar N. An integrated system for detection exudates and severity quantification for diabetic macular edema. J Med Biol Eng 2020;40(6):798–820.
   Web of Science ®Google Scholar
• Maurya PK, Gupta V, Singh M, et al. Automated detection of diabetic macular edema involving cystoids and serous retinal detachment. Opt Laser Technol 2020;127(106157):106157.
   Google Scholar
• Rajput GG, Reshmi BM, Rajesh IS. Automatic detection and grading of diabetic maculopathy using fundus images. Procedia Comput Sci. 2020;167:57–66.
   Google Scholar
• Shahid AH, Singh MP. A deep learning approach for prediction of Parkinson’s disease progression. Biomed Eng Lett 2020;10(2):227–239.
   PubMed Web of Science ®Google Scholar
• Varadarajan AV, Bavishi P, Ruamviboonsuk P, et al. Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning. Nat Commun. 2020;11(1):130.
   PubMed Web of Science ®Google Scholar
• Abedalla A, Abdullah M, Al-Ayyoub M, et al. Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures. PeerJ Comput Sci. 2021;7(e607):e607.
   PubMedGoogle Scholar
• Wang Z, Zhong Y, Yao M, et al. Automated segmentation of macular edema for the diagnosis of ocular disease using deep learning method. Sci Rep 2021;11(1):13392.
   PubMed Web of Science ®Google Scholar
• Ajaz A, Kumar H, Kumar D. A review of methods for automatic detection of macular edema. Biomed Signal Process Control. 2021;69(102858):102858.
   Google Scholar
• Khalid S, Akram MU, Shehryar T, et al. Automated diagnosis system for age-related macular degeneration using hybrid features set from fundus images. Int J Imaging Syst Technol 2021;31(1):236–252.
   Web of Science ®Google Scholar
• Mathews MR, Anzar SM. A comprehensive review on automated systems for severity grading of diabetic retinopathy and macular edema. Int J Imaging Syst Technol 2021;31(4):2093–2122.
   Web of Science ®Google Scholar
• Bengani S, Jothi AA, Vadivel S. Automatic segmentation of optic disc in retinal fundus images using semi-supervised deep learning. Multimed Tools Appl. 2021;80(3):3443–3468.
   Web of Science ®Google Scholar
• Tao Z, Zhang W, Yao M, et al. A joint model for macular edema analysis in optical coherence tomography images based on image enhancement and segmentation. BioMed Res Int 2021;2021:6679556.
   PubMed Web of Science ®Google Scholar
• Chalakkal R, Hafiz F, Abdulla W, et al. An efficient framework for automated screening of Clinically Significant Macular Edema. Comput Biol Med 2021;130(104128):104128.
   PubMedGoogle Scholar
• Rehman MU, Cho S, Kim J, et al. BrainSeg-Net: brain tumor MR image segmentation via enhanced encoder-decoder network. Diagnostics (Basel). 2021;11(2):169.
   PubMedGoogle Scholar
• Liu X, Ali TK, Singh P, et al. Deep learning to detect OCT-derived diabetic macular edema from color retinal photographs: a multicenter validation study. Ophthalmology Retina. 2022;6(5):398–410.
   PubMedGoogle Scholar
• Fu Y, Zhang G, Li J, et al. Fovea localization by blood vessel vector in abnormal fundus images,”. Pattern Recognit. 2022;129:108711.
   Web of Science ®Google Scholar
• Azad R, Heidari M, Shariatnia M, et al. TransDeepLab: convolution-free transformer-based DeepLab v3 + for medical image segmentation. arXiv [eess.IV]. 2022.
   Google Scholar
• Udayaraju P, Jeyanthi P, Sekhar BV. A hybrid multilayered classification model with VGG-19 net for retinal diseases using optical coherence tomography images. Soft comput. Sep 2023;27(17):12559–12570.
   Web of Science ®Google Scholar
• Fu Y, Lu X, Zhang G, et al. Automatic grading of diabetic macular edema based on end-to-end network. Expert Syst Appl. 2023;213(1):118835.
   Google Scholar
• Babu PA, Rai AK, Ramesh JV, et al. An explainable deep learning approach for oral cancer detection. J Electr Eng Technol. 2023: 1–2.
   PubMed Web of Science ®Google Scholar
• Zuiderveld KJ. Contrast limited adaptive histogram equalization. In: Graphics gems. 1st ed. San Diego, CA: Academic Press Professional, Inc.; 1994. p. 474–485.
   Google Scholar
• Gao W, Peng M, Wang H, et al. Incorporating word embeddings into topic modeling of short text. Knowl Inf Syst 2019;61(2):1123–1145.
   Web of Science ®Google Scholar
• AbdelMaksoud E, Barakat S, Elmogy M. A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique. Med Biol Eng Comput 2022;60(7):2015–2038.
   PubMed Web of Science ®Google Scholar