{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T18:17:18Z","timestamp":1789669038395,"version":"4.0.1"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T00:00:00Z","timestamp":1672358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Diabetic retinopathy (DR) is an ophthalmological disease that causes damage in the blood vessels of the eye. DR causes clotting, lesions or haemorrhage in the light-sensitive region of the retina. Person suffering from DR face loss of vision due to the formation of exudates or lesions in the retina. The detection of DR is critical to the successful treatment of patients suffering from DR. The retinal fundus images may be used for the detection of abnormalities leading to DR. In this paper, an automated ensemble deep learning model is proposed for the detection and classification of DR. The ensembling of a deep learning model enables better predictions and achieves better performance than any single contributing model. Two deep learning models, namely modified DenseNet101 and ResNeXt, are ensembled for the detection of diabetic retinopathy. The ResNeXt model is an improvement over the existing ResNet models. The model includes a shortcut from the previous block to next block, stacking layers and adapting split\u2013transform\u2013merge strategy. The model has a cardinality parameter that specifies the number of transformations. The DenseNet model gives better feature use efficiency as the dense blocks perform concatenation. The ensembling of these two models is performed using normalization over the classes followed by maximum a posteriori over the class outputs to compute the final class label. The experiments are conducted on two datasets APTOS19 and DIARETDB1. The classifications are carried out for both two classes and five classes. The images are pre-processed using CLAHE method for histogram equalization. The dataset has a high-class imbalance and the images of the non-proliferative type are very low, therefore, GAN-based augmentation technique is used for data augmentation. The results obtained from the proposed method are compared with other existing methods. The comparison shows that the proposed method has higher accuracy, precision and recall for both two classes and five classes. The proposed method has an accuracy of 86.08 for five classes and 96.98% for two classes. The precision and recall for two classes are 0.97. For five classes also, the precision and recall are high, i.e., 0.76 and 0.82, respectively.<\/jats:p>","DOI":"10.3390\/diagnostics13010124","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T02:12:48Z","timestamp":1672625568000},"page":"124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":119,"title":["EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy"],"prefix":"10.3390","volume":"13","author":[{"given":"Sambit S.","family":"Mondal","sequence":"first","affiliation":[{"name":"Department of Electronics & Communication Engineering, Asansol Engineering College, Asansol 713305, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nirupama","family":"Mandal","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, IIT (ISM) Dhanbad, Dhanbad 826004, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6510-6768","authenticated-orcid":false,"given":"Krishna Kant","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of CSE, ASET, Amity University, Noida 201301, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akansha","family":"Singh","sequence":"additional","affiliation":[{"name":"School of CSET, Bennett University, Greater Noida 201310, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9761-0096","authenticated-orcid":false,"given":"Ivan","family":"Izonin","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79000 Lviv, Ukraine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.35841\/biomedicalresearch.30-19-068","article-title":"Optimized maximum principal curvatures based segmentation of blood vessels from retinal images","volume":"30","author":"Kumar","year":"2019","journal-title":"Biomed. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1016\/j.procs.2015.09.005","article-title":"Retinal blood vessel segmentation approach based on mathematical morphology","volume":"65","author":"Hassan","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TBME.2016.2535311","article-title":"A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images","volume":"64","author":"Orlando","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2060","DOI":"10.1016\/j.procs.2020.03.246","article-title":"Blood vessel detection from Retinal fundas images using GIFKCN classifier","volume":"167","author":"Mondal","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1007\/s40846-018-0454-2","article-title":"Retinal blood vessel segmentation by using matched filtering and fuzzy c-means clustering with integrated level set method for diabetic retinopathy assessment","volume":"39","author":"Memari","year":"2019","journal-title":"J. Med. Biol. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"154860","DOI":"10.1155\/2013\/154860","article-title":"Robust vessel segmentation in fundus images","volume":"2013","author":"Budai","year":"2013","journal-title":"Int. J. Biomed. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1109\/TMI.2015.2496296","article-title":"Superpixel-based segmentation for 3D prostate MR images","volume":"35","author":"Tian","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.patrec.2020.02.026","article-title":"Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model","volume":"133","author":"Shankar","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ghosh, R., Ghosh, K., and Maitra, S. (2017, January 2\u20133). Automatic detection and classification of diabetic retinopathy stages using CNN. Proceedings of the 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India.","DOI":"10.1109\/SPIN.2017.8050011"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102115","DOI":"10.1016\/j.bspc.2020.102115","article-title":"A lightweight CNN for Diabetic Retinopathy classification from fundus images","volume":"62","author":"Gayathri","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9704","DOI":"10.1038\/s41598-021-89225-0","article-title":"Understanding inherent image features in CNN-based assessment of diabetic retinopathy","volume":"11","author":"Reguant","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Benson, J., Carrillo, H., Wigdahl, J., Nemeth, S., Maynard, J., Zamora, G., Barriga, S., Estrada, T., and Soliz, P. (2018, January 10\u201315). Transfer learning for diabetic retinopathy. Proceedings of the Medical Imaging 2018: Image Processing, Houston, TX, USA.","DOI":"10.1117\/12.2293378"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Gadekallu, T.R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P.K.R., Ra, I.H., and Alazab, M. (2020). Early detection of diabetic retinopathy using PCA-firefly based deep learning model. Electronics, 9.","DOI":"10.3390\/electronics9020274"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kandel, I., and Castelli, M. (2020). Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Appl. Sci., 10.","DOI":"10.3390\/app10062021"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gadekallu, T.R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P.K.R., and Srivastava, G. (2020). Deep neural networks to predict diabetic retinopathy. J. Ambient. Intell. Humaniz. Comput., 1\u201314.","DOI":"10.1007\/s12652-020-01963-7"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shen, Z., Wu, Q., Wang, Z., Chen, G., and Lin, B. (2021). Diabetic Retinopathy Prediction by Ensemble Learning Based on Biochemical and Physical Data. Sensors, 21.","DOI":"10.3390\/s21113663"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Reddy, G.T., Bhattacharya, S., Ramakrishnan, S.S., Chowdhary, C.L., Hakak, S., Kaluri, R., and Reddy, M.P.K. (2020, January 24\u201325). An ensemble based machine learning model for diabetic retinopathy classification. Proceedings of the 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India.","DOI":"10.1109\/ic-ETITE47903.2020.235"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ali, R., Hardie, R.C., Narayanan, B.N., and Kebede, T.M. (2022). IMNets: Deep Learning Using an Incremental Modular Network Synthesis Approach for Medical Imaging Applications. Appl. Sci., 12.","DOI":"10.3390\/app12115500"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kobat, S.G., Baygin, N., Yusufoglu, E., Baygin, M., Barua, P.D., Dogan, S., Orhan, Y., Celiker, U., Yildirim, H., and Tan, R.-S. (2022). Automated diabetic retinopathy detection using horizontal and vertical patch division-based pre-trained DenseNET with digital fundus images. Diagnostics, 12.","DOI":"10.3390\/diagnostics12081975"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lim, G., Thombre, P., Lee, M.L., and Hsu, W. (2020, January 9\u201311). Generative Data Augmentation for Diabetic Retinopathy Classification. Proceedings of the 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), Baltimore, MD, USA.","DOI":"10.1109\/ICTAI50040.2020.00167"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_22","unstructured":"Hitawala, S. (2018). Evaluating ResNeXt Model Architecture for Image Classification. arXiv."},{"key":"ref_23","first-page":"1","article-title":"The diaretdb1 diabetic retinopathy database and evaluation protocol","volume":"1","author":"Kauppi","year":"2007","journal-title":"BMVC"},{"key":"ref_24","unstructured":"Aravind Eye Hospital (2022, November 19). APTOS 2019 Blindness Detection. Available online: https:\/\/www.kaggle.com\/c\/aptos2019-blindness-detection."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1007\/s11760-021-01904-7","article-title":"DRISTI: A hybrid deep neural network for diabetic retinopathy diagnosis","volume":"15","author":"Kumar","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104795","DOI":"10.1016\/j.compbiomed.2021.104795","article-title":"Simple methods for the lesion detection and severity grading of diabetic retinopathy by image processing and transfer learning","volume":"137","author":"Sugeno","year":"2021","journal-title":"Comput. Biol. Med."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/1\/124\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:56:23Z","timestamp":1760147783000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/1\/124"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,30]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["diagnostics13010124"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13010124","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,30]]}}}