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CTNet: convolutional transformer network for diabetic retinopathy classification

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Abstract

Currently, diabetic retinopathy diagnosis tools use deep learning and machine learning algorithms for fundus image classification. Deep learning techniques especially convolution neural networks (CNNs) showed outstanding results in the area of lesion detection, segmentation, and diabetic retinopathy classification. Despite high performance, CNNs focus on spatial locality due to strong spatial learning bias and ignore long-range perspectives. To address this issue, the use of transformers is evolving in the computer vision domain. The present work proposes a lightweight diabetic retinopathy classification method—CTNet, using the combination of CNN and Transformers on fundus images to capture both local and global spatial features. Specifically, first, a convolution module is designed with residual connections for extracting local lesion features. Then a transformer module patchifies these features into a sequence of small patches and determines a global long-range perspective focusing on how much focus one lesion places on other lesions of the sequence using self-attention. Finally, pooling is performed on a sequence of patches instead of using memory-inefficient class tokens to generate a single index for classification. The proposed CTNet model requires 823,555 parameters and obtains consistent performance of (0.987 AUC, 0.972 Kappa), and (0.990 AUC, 0.975 Kappa) scores on APTOS and IDRiD datasets, respectively.

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References

  1. WHO (2021) Update from the seventy-fourth world health assembly, 28 May 2021. https://www.who.int/news/item/28-05-2021-update-from-the-seventy-fourth-world-health-assembly-28-may-2021. Accessed on 28 Feb 2022

  2. Raman R, Vasconcelos J, Rajalakshmi R, Prevost A, Ramasamy K, Mohan V, Mohan D, Rani P, Conroy D, Das T, Sivaprasad S (2022) SMART India Study Collaborators. Prevalence of diabetic retinopathy in India stratified by known and undiagnosed diabetes, urban-rural locations, and socioeconomic indices: results from the SMART India population-based cross-sectional screening study. Lancet Glob Health https://doi.org/10.1016/S2214-109X(22)00411-9

  3. Bala R, Sharma A, Goel N (2023) Comparative analysis of diabetic retinopathy classification approaches using machine learning and deep learning techniques. Arch Computat Methods Eng. https://doi.org/10.1007/s11831-023-10002-5

    Article  Google Scholar 

  4. Thomas R, Halim S, Gurudas S, Sivaprasad S, Owens D (2019) IDF diabetes atlas: a review of studies utilising retinal photography on the global prevalence of diabetes related retinopathy between 2015 and 2018. Diabetes Res Clin Prac 157:107840. https://doi.org/10.1016/j.diabres.2019.107840

    Article  CAS  Google Scholar 

  5. Huang X, Wang H, Xue W, Xiang S, Huang H, Meng L, Ma G, Ullah A, Zhang G (2020) Study on time-temperature-transformation diagrams of stainless steel using machine-learning approach. Comput Mater Sci 171:109282. https://doi.org/10.1016/j.commatsci.2019.109282

    Article  CAS  Google Scholar 

  6. Nazir S, Nawaz Khan M, Anwar S, Adnan A, Asadi S, Shahzad S, Ali S (2019) Big data visualization in cardiology-a systematic review and future directions. IEEE Access 7:115945–115958. https://doi.org/10.1109/ACCESS.2019.2936133

    Article  Google Scholar 

  7. Jan S, Musa S, Syed T, Nauman M, Anwar S, Tanveer T, Shah B (2020) Integrity verification and behavioral classification of a large dataset applications pertaining smart OS via blockchain and generative models. Expert Syst https://doi.org/10.1111/exsy.12611

  8. Huang X, Wang H, Xue W, Ullah A, Xiang S, Huang H, Meng L, Ma G, Zhang G (2020) A combined machine learning model for the prediction of time-temperature-transformation diagrams of high-alloy steels. J Alloys Compd 823:153694. https://doi.org/10.1016/j.jallcom.2020.153694

    Article  CAS  Google Scholar 

  9. Tufail A, Kapetanakis VV, Salas-Vega S, Egan C, Rudisill C, Owen CG, Lee A, Louw V, Anderson J, Liew G, Bolter L, Bailey C, Sadda S, Taylor P, Rudnicka AR (2016) An observational study to assess if automated diabetic retinopathy image assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technol Assess https://doi.org/10.3310/hta20920

  10. Bishnoi V, Goel N (2023) A color-based deep-learning approach for tissue slide lung cancer classification. Biomed Signal Process Control 86:105151. https://doi.org/10.1016/j.bspc.2023.105151

    Article  Google Scholar 

  11. Geng X, Mao X, Wu H-H, Wang S, Xue W, Zhang G, Ullah A, Wang H (2022) A hybrid machine learning model for predicting continuous cooling transformation diagrams in welding heat-affected zone of low alloy steels. J Mater Sci Technol 107:207–215. https://doi.org/10.1016/j.jmst.2021.07.038

    Article  CAS  Google Scholar 

  12. Abràmoff MD, Reinhardt JM, Russell SR, Folk JC, Mahajan VB, Niemeijer M, Quellec G (2010) Automated early detection of diabetic retinopathy. Ophthalmology 117:1147–1154. https://doi.org/10.1016/j.ophtha.2010.03.046

    Article  PubMed  Google Scholar 

  13. Yau JWY, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, Chen S-J, Dekker JM, Fletcher A, Grauslund J, Haffner S, Hamman RF, Ikram MK, Kayama T, Klein BEK, Klein R, Krishnaiah S, Mayurasakorn K, O’Hare JP, Orchard TJ, Porta M, Rema M, Roy MS, Sharma T, Shaw J, Taylor H, Tielsch JM, Varma R, Wang JJ, Wang N, West S, Xu L, Yasuda M, Zhang X, Mitchell P, Wong TY, for Eye Disease (META-EYE) Study Group, M-A (2012) Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35:556–564. https://doi.org/10.2337/dc11-1909

  14. Solanki K, Ramachandra CA, Bhat S, Bhaskaranand M, Nittala MG, Sadda SR (2015) EyeArt: automated, high-throughput, image analysis for diabetic retinopathy screening. Investig Ophthalmol Vis Sci 56:1429–1429

    Google Scholar 

  15. Halim Z, Khan G, Shah B, Naseer R, Anwar S, Shah A (2023) On the utility of parents’ historical data to investigate the causes of autism spectrum disorder: a data mining-based framework. IRBM 44(4):100780. https://doi.org/10.1016/j.irbm.2023.100780

    Article  Google Scholar 

  16. Geng X, Wang H, Ullah A, Xue W, Xiang S, Meng L, Ma G (2020) Prediction of continuous cooling transformation diagrams for Ni–Cr–Mo welding steels via machine learning approaches. JOM 72:3926–3934. https://doi.org/10.1007/s11837-020-04057-z

    Article  CAS  ADS  Google Scholar 

  17. Philip S, Fleming AD, Goatman KA, Fonseca S, Mcnamee P, Scotland GS, Prescott GJ, Sharp PF, Olson JA (2007) The efficacy of automated disease/no disease grading for diabetic retinopathy in a systematic screening programme. Br J Ophthalmol 91:1512–1517. https://doi.org/10.1136/bjo.2007.11945

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Abràmoff MD, Folk JC, Han DP, Walker JD, Williams DF, Russell SR, Massin P, Cochener B, Gain P, Tang L, Lamard M, Moga DC, Quellec G, Niemeijer M (2013) Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol 131:351–357. https://doi.org/10.1001/jamaophthalmol.2013.1743

    Article  PubMed  Google Scholar 

  19. Wilkinson CP, Ferris FL, Klein RE, Lee PP, Agardh CD, Davis M, Dills D, Kampik A, Pararajasegaram R, Verdaguer JT, Lum F (2003) Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology 110:1677–1682. https://doi.org/10.1016/S0161-6420(03)00475-5

    Article  CAS  PubMed  Google Scholar 

  20. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA J Am Med Assoc 316:2402–2410. https://doi.org/10.1001/jama.2016.17216

    Article  Google Scholar 

  21. Wan S, Liang Y, Zhang Y (2018) Deep convolutional neural networks for diabetic retinopathy detection by image classification. Comput Electr Eng 72:274–282. https://doi.org/10.1016/j.compeleceng.2018.07.042

    Article  Google Scholar 

  22. Sayres R, Taly A, Rahimy E, Blumer K, Coz D, Hammel N, Krause J, Narayanaswamy A, Rastegar Z, Wu D, Xu S, Barb S, Joseph A, Shumski M, Smith J, Sood AB, Corrado GS, Peng L, Webster DR (2019) Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology 126:552–564. https://doi.org/10.1016/j.ophtha.2018.11.01

    Article  PubMed  Google Scholar 

  23. Wang X-N, Dai L, Li S-T, Kong H-Y, Sheng B, Wu Q (2020) Automatic grading system for diabetic retinopathy diagnosis using deep learning artificial intelligence software. Curr Eye Res 45(12):1550–1555. https://doi.org/10.1080/02713683.2020.176497

  24. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929

  25. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. arXiv:1706.03762

  26. Bala R, Sharma A, Goel N (2022) Classification of fundus images for diabetic retinopathy using machine learning: a brief review. Adv Intell Syst Comput https://doi.org/10.1007/978-981-16-6887-6_4

  27. Safitri DW, Juniati D (2017) Classification of diabetic retinopathy using fractal dimension analysis of eye fundus image. In: AIP conference proceedings vol 1867, no. 020011 https://doi.org/10.1063/1.4994414

  28. Carrera EV, González A, Carrera R (2017) Automated detection of diabetic retinopathy using SVM. In: 2017 IEEE XXIV international conference on electronics, electrical engineering and computing (INTERCON), pp 1–4. https://doi.org/10.1109/INTERCON.2017.807969

  29. Leeza M, Farooq H (2019) Detection of severity level of diabetic retinopathy using bag of features model. IET Comput Vis 13:523–530. https://doi.org/10.1049/iet-cvi.2018.526

    Article  Google Scholar 

  30. Sikder N, Masud M, Bairagi AK, Arif ASM, Nahid AA, Alhumyani HA (2021) Severity classification of diabetic retinopathy using an ensemble learning algorithm through analyzing retinal images. Symmetry https://doi.org/10.3390/sym1304067

  31. Odeh I, Alkasassbeh M, Alauthman M (2021) Diabetic retinopathy detection using ensemble machine learning. arXiv:2106.12545

  32. Mahendran G, Dhanasekaran R (2015) Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms. Comput Electric Eng 45:312–323. https://doi.org/10.1016/j.compeleceng.2015.01.013

    Article  Google Scholar 

  33. Bala R, Sharma A, Goel N (2022) A lightweight deep learning approach for diabetic retinopathy classification. In: Artificial intelligence and speech technology. Springer, Cham, pp 277–287. https://link.springer.com/chapter/10.1007/978-3-030-95711-7_25

  34. Ghosh R, Ghosh K, Maitra S (2017) Automatic detection and classification of diabetic retinopathy stages using CNN. In: 2017 4th International conference on signal processing and integrated networks (SPIN), pp 550–554. https://doi.org/10.1109/SPIN.2017.8050011

  35. Shaban M, Ogur Z, Mahmoud A, Switala A, Shalaby A, Khalifeh HA, Ghazal M, Fraiwan L, Giridharan G, Sandhu H, El-Baz AS (2020) A convolutional neural network for the screening and staging of diabetic retinopathy. PLoS ONE https://doi.org/10.1371/journal.pone.0233514

  36. Hemanth DJ, Deperlioglu O, Kose U (2020) An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Comput Appl 32:707–721. https://doi.org/10.1007/s00521-018-03974-0

    Article  Google Scholar 

  37. Maistry A, Pillay A, Jembere E (2020) Improving the accuracy of diabetes retinopathy image classification using augmentation. In: Conference of the South African institute of computer scientists and information technologists 2020. SAICSIT ’20. Association for Computing Machinery, New York, pp 134–140. https://doi.org/10.1145/3410886.3410914

  38. Xu K, Feng D, Mi H (2017) Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules https://doi.org/10.3390/molecules2212205

  39. Riaz H, Park J, Choi H, Kim H, Kim J (2020) Deep and densely connected networks for classification of diabetic retinopathy. Diagnostics https://doi.org/10.3390/diagnostics10010024

  40. Hossen MS, Reza AA, Mishu MC (2020) An automated model using deep convolutional neural network for retinal image classification to detect diabetic retinopathy. PervasiveHealth: Pervasive Computing Technologies for Healthcare, https://doi.org/10.1145/3377049.3377067

  41. Shankar K, Sait ARW, Gupta D, Lakshmanaprabu SK, Khanna A, Pandey HM (2020) Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recogn Lett 133:210–216. https://doi.org/10.1016/j.patrec.2020.02.026

    Article  ADS  Google Scholar 

  42. Martinez-Murcia FJ, Ortiz A, Ramírez J, Górriz JM, Cruz R (2021) Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy. Neurocomputing 452:424–434. https://doi.org/10.1016/j.neucom.2020.04.148

    Article  Google Scholar 

  43. Adriman R, Muchtar K, Maulina N (2021) Performance evaluation of binary classification of diabetic retinopathy through deep learning techniques using texture feature, vol 179. Elsevier, pp 88–94. https://doi.org/10.1016/j.procs.2021.12.012

  44. Habib Raj MA, Mamun MA, Faruk MF (2020) CNN based diabetic retinopathy status prediction using fundus images. In: 2020 IEEE region 10 symposium (TENSYMP), pp 190–193. https://doi.org/10.1109/TENSYMP50017.2020.9230974

  45. Tymchenko B, Marchenko P, Spodarets D (2020) Deep learning approach to diabetic retinopathy detection. arXiv:2003.02261

  46. Jiang H, Yang K, Gao M, Zhang D, Ma H, Qian W (2019) An interpretable ensemble deep learning model for diabetic retinopathy disease classification. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2045–2048. https://doi.org/10.1109/EMBC.2019.8857160

  47. Sharmin M, Nasser K (2021) Multitasking deep learning model for detection of five stages of diabetic retinopathy. IEEE Access 9:123220–123230. https://doi.org/10.1109/ACCESS.2021.3109240

    Article  Google Scholar 

  48. Bodapati JD, Veeranjaneyulu N, Shareef SN, Hakak S, Bilal M, Maddikunta PKR, Jo O (2020) Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electronics https://doi.org/10.3390/electronics9060914

  49. Pour AM, Seyedarabi H, Jahromi SHA, Javadzadeh A (2020) Automatic detection and monitoring of diabetic retinopathy using efficient convolutional neural networks and contrast limited adaptive histogram equalization. IEEE Access 8:136668–136673. https://doi.org/10.1109/ACCESS.2020.3005044

    Article  Google Scholar 

  50. Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, Kaski K (2019) Deep learning fundus image analysis for diabetic retinopathy and macular edema grading. Sci Rep https://doi.org/10.1038/s41598-019-47181-w

  51. Adak C, Karkera T, Chattopadhyay S, Saqib M (2023) Detecting severity of diabetic retinopathy from fundus images using ensembled transformers. arXiv:2301.00973

  52. Li X, Hu X, Yu L, Zhu L, Fu CW, Heng PA (2020) CANet: cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Trans Med Imaging 39:1483–1493. https://doi.org/10.1109/TMI.2019.2951844

    Article  CAS  PubMed  Google Scholar 

  53. Farag MM, Fouad M, Abdel-Hamid AT (2022) Automatic severity classification of diabetic retinopathy based on densenet and convolutional block attention module. IEEE Access 10:38299–38308. https://doi.org/10.1109/ACCESS.2022.3165193

    Article  Google Scholar 

  54. Erciyas A, Barişçi N (2021) An effective method for detecting and classifying diabetic retinopathy lesions based on deep learning. Comput Math Methods Med https://doi.org/10.1155/2021/9928899

  55. He A, Li T, Li N, Wang K, Fu H (2021) CABNet: category attention block for imbalanced diabetic retinopathy grading. IEEE Trans Med Imaging 40(1):143–153. https://doi.org/10.1109/TMI.2020.3023463

    Article  PubMed  Google Scholar 

  56. Zhe W, Yanxin Y, Jianping S, Wei F, Hongsheng L, Xiaogang W (2017) Zoom-in-net: deep mining lesions for diabetic retinopathy detection. In: Maxime D, Lena M-H, Alfred F, Pierre J, Louis CD, Simon D (eds) Medical image computing and computer assisted intervention—MICCAI 2017. Springer, Cham, pp 267–275

    Google Scholar 

  57. Wu Z, Shi G, Chen Y, Shi F, Chen X, Coatrieux G, Yang J, Luo LM, Li S (2020) Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network. Artif Intell Med https://doi.org/10.1016/j.artmed.2020.101936

  58. Kaggle (2019) APTOS 2019 Blindness detection. https://www.kaggle.com/c/aptos2019-blindness-detection/data

  59. Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F (2018) Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. https://doi.org/10.21227/H25W98

  60. Lee SH, Lee S, Song BC (2021) Vision transformer for small-size datasets. arXiv:2112.13492

  61. Heo B, Yun S, Han D, Chun S, Choe J, Oh SJ (2021) Rethinking spatial dimensions of vision transformers. CoRR arxiv:2103.16302

  62. Yao T, Li Y, Pan Y, Wang Y, Zhang X-P, Mei T (2023) Dual vision transformer. IEEE transactions on pattern analysis and machine intelligence 45(9):10870–10882. https://doi.org/10.1109/TPAMI.2023.3268446

    Article  PubMed  Google Scholar 

  63. Fan H, Xiong B, Mangalam K, Li Y, Yan Z, Malik J, Feichten- hofer C (2021) Multiscale Vision Transformers. arXiv:2104.11227

  64. Li Y, Wu C-Y, Fan H, Mangalam K, Xiong B, Malik J, Feicht- enhofer C (2022) MViTv2: Improved Multiscale Vision Transformers for Classification and Detection. arXiv:2112.01526

  65. Hassani A, Walton S, Shah N, Abuduweili A, Li J, Shi H (2021) Escaping the big data paradigm with compact transformers. arXiv:2104.05704

  66. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  67. Brody S, Alon U, Yahav E (2023) On the Expressivity Role of LayerNorm in Transformers’ Attention. arXiv:2305.02582

  68. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2818–2826. https://doi.org/10.1109/CVPR.2016.308

  69. Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR). IEEE Computer Society, Los Alamitos, pp 2261–2269. https://doi.org/10.1109/CVPR.2017.24

  70. Christian S, Sergey I, Vincent V, Alexander A (2016) Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI conference on artificial intelligence, vol 31. https://doi.org/10.1609/aaai.v31i1.11231

  71. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 1800–1807. https://doi.org/10.1109/CVPR.2017.19

  72. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. CoRR arXiv:1409.1556

  73. Iandola FN, Moskewicz MW, Ashraf K, Han S, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and \(<\)1mb model size. CoRR arxiv:1602.07360

  74. Zhang X, Zhou X, Lin M, Sun J (2017) Shufflenet: An extremely efficient convolutional neural network for mobile devices. CoRR arXiv:1707.01083

  75. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861

  76. Zoph B, Vasudevan V, Shlens J, Le QV (2017) Learning transferable architectures for scalable image recognition. CoRR arXiv:1707.07012

  77. Tan M, Le Q V (2020) EfficientNet: Rethinking model scaling for convolutional neural networks. arXiv:1905.11946

  78. Ketkar N (2017) Stochastic gradient descent, pp 111–130. https://doi.org/10.1007/978-1-4842-2766-4_8

  79. Kingma D P, Ba J (2017) Adam: A Method for Stochastic Optimization. arXiv:1412.6980

  80. Foret P, Kleiner A, Mobahi H, Neyshabur B (2021) Sharpness-Aware Minimization for Efficiently Improving Generalization. arXiv:2010.01412

  81. Loshchilov I, Hutter F (2019) Decoupled Weight Decay Regularization. arXiv:1711.05101

  82. Butt MM, Iskandar DNFA, Abdelhamid SE, Latif G, Alghazo R (2022) Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features. Diagnostics https://doi.org/10.3390/diagnostics12071607

  83. Wu Z, Shi G, Chen Y, Shi F, Chen X, Coatrieux G, Yang J, Luo L, Li S (2020) Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network. Artif Intell Med 108:101936. https://doi.org/10.1016/j.artmed.2020.101936

    Article  PubMed  Google Scholar 

  84. Athira TR, Sivadas A, George A, Paul A, Gopan NR (2019) Automatic detection of diabetic retinopathy using R-CNN. Int Res J Eng Technol 5595

  85. Gayathri S, Gopi VP, Palanisamy P (2020) Automated classification of diabetic retinopathy through reliable feature selection. Phys Eng Sci Med 43:927–945. https://doi.org/10.1007/s13246-020-00890-3

    Article  CAS  PubMed  Google Scholar 

  86. Zago GT, Andreão RV, Dorizzi B, Teatini Salles EO (2020) Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Comput Biol Med 116:103537. https://doi.org/10.1016/j.compbiomed.2019.103537

    Article  PubMed  Google Scholar 

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  1. Department of Information Technology, IGDTUW, Delhi, New Delhi, 110006, India

    Ruchika Bala & Arun Sharma

  2. Department of Electronics and Communications, IGDTUW, Delhi, New Delhi, 110006, India

    Nidhi Goel

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Bala, R., Sharma, A. & Goel, N. CTNet: convolutional transformer network for diabetic retinopathy classification. Neural Comput & Applic 36, 4787–4809 (2024). https://doi.org/10.1007/s00521-023-09304-3

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