{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T17:32:07Z","timestamp":1791394327532,"version":"4.3.3"},"reference-count":26,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Universidad Peruana de Ciencias Aplicadas","award":["UPC-EXPOST-2024-2"],"award-info":[{"award-number":["UPC-EXPOST-2024-2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>In this article, various convolutional neural network (CNN) architectures for the detection of pterygium in the anterior segment of the eye are explored and compared. Five CNN architectures (ResNet101, ResNext101, Se-ResNext50, ResNext50, and MobileNet V2) are evaluated with the objective of identifying one that surpasses the precision and diagnostic efficacy of the current existing solutions. The results show that the Se-ResNext50 architecture offers the best overall performance in terms of precision, recall, and accuracy, with values of 93%, 92%, and 92%, respectively, for these metrics. These results demonstrate its potential to enhance diagnostic tools in ophthalmology.<\/jats:p>","DOI":"10.3390\/diagnostics14182026","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T04:25:22Z","timestamp":1726201522000},"page":"2026","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9389-7430","authenticated-orcid":false,"given":"Maria Isabel","family":"Moreno-Lozano","sequence":"first","affiliation":[{"name":"Information Systems Engineering Faculty, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8538-3147","authenticated-orcid":false,"given":"Edward Jordy","family":"Ticlavilca-Inche","sequence":"additional","affiliation":[{"name":"Software Engineering Faculty, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1865-1293","authenticated-orcid":false,"given":"Pedro","family":"Casta\u00f1eda","sequence":"additional","affiliation":[{"name":"Information Systems Engineering Faculty, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6154-2124","authenticated-orcid":false,"given":"Sandra","family":"Wong-Durand","sequence":"additional","affiliation":[{"name":"Information Systems Engineering Faculty, Universidad Peruana de Ciencias Aplicadas, Lima 15023, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Mauricio","sequence":"additional","affiliation":[{"name":"Systems Engineering and Informatic Faculty, Universidad Nacional Mayor de San Marcos (UNMSM), Lima 15081, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alejandra","family":"O\u00f1ate-Andino","sequence":"additional","affiliation":[{"name":"Informatic and Electronics Faculty, Escuela Superior Polit\u00e9cnica de Chimborazo (ESPOCH), Riobamba 060155, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Abdani, S.R., Zulkifley, M.A., and Zulkifley, N.H. (2021). Group and Shuffle Convolutional Neural Networks with Pyramid Pooling Module for Automated Pterygium Segmentation. Diagnostics, 11.","DOI":"10.3390\/diagnostics11061104"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.cmpb.2017.10.026","article-title":"Automated pterygium detection method of anterior segment photographed images","volume":"154","author":"Zaki","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.survophthal.2022.08.005","article-title":"Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review","volume":"68","author":"Coan","year":"2023","journal-title":"Surv. Ophthalmol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1136\/bjo-2022-322552","article-title":"Accurate detection and grading of pterygium through smartphone by a fusion training model","volume":"108","author":"Liu","year":"2023","journal-title":"Br. J. Ophthalmol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.1136\/bjophthalmol-2021-318866","article-title":"Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras","volume":"106","author":"Fang","year":"2021","journal-title":"Br. J. Ophthalmol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"115","DOI":"10.3991\/ijoe.v20i08.48421","article-title":"Mobile Application Based on Convolutional Neural Networks for Pterygium Detection in Anterior Segment Eye Images at Ophthalmological Medical Centers","volume":"20","year":"2024","journal-title":"Int. J. Online Biomed. Eng. (iJOE)"},{"key":"ref_7","unstructured":"(2024, January 08). Centro Peruano del Pterigion\u2014Somos el Primer Centro Especializa-do en el Tratamiento Integral del Pterigion en Per\u00fa. Available online: https:\/\/carnosidadperu.com\/."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"191659","DOI":"10.1109\/ACCESS.2020.3030787","article-title":"Automated pterygium detection using deep neu-ral network","volume":"8","author":"Zamani","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"225","DOI":"10.30534\/ijatcse\/2019\/3481.62019","article-title":"Automated pterygium detection in anterior segment photo-graphed images using deep convolutional neural network","volume":"8","author":"Saad","year":"2019","journal-title":"Int. J. Adv. Trends Comput. Sci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"99","DOI":"10.17576\/jkukm-2019-31(1)-12","article-title":"Sistem saringan penyakit pterigium untuk imej mata terangkum hadapan","volume":"31","author":"Ahmad","year":"2019","journal-title":"J. Kejuruteraan"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"85","DOI":"10.14419\/ijet.v7i4.11.20780","article-title":"Mobile screening framework of anterior segment photo-graphed images","volume":"7","author":"Zamani","year":"2018","journal-title":"Int. J. Eng. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hung, K.-H., Lin, C., Roan, J., Kuo, C.-F., Hsiao, C.-H., Tan, H.-Y., Chen, H.-C., Ma, D.H.-K., Yeh, L.-K., and Lee, O.K.-S. (2022). Application of a Deep Learning System in Pterygium Grading and Further Prediction of Recurrence with Slit Lamp Photographs. Diagnostics, 12.","DOI":"10.3390\/diagnostics12040888"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3942110","DOI":"10.1155\/2022\/3942110","article-title":"Pterygium Screening and Lesion Area Segmentation Based on Deep Learning","volume":"2022","author":"Zhu","year":"2022","journal-title":"J. Health Eng."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Reguant, R., Brunak, S., and Saha, S. (2021). Understanding inherent image features in CNN-based assessment of diabetic retinopathy. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-89225-0"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1016\/j.ophtha.2020.12.020","article-title":"Assessing Glaucoma Progression Using Machine Learning Trained on Longitudinal Visual Field and Clinical Data","volume":"128","author":"Dixit","year":"2021","journal-title":"Ophthalmology"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yeh, T.-C., Luo, A.-C., Deng, Y.-S., Lee, Y.-H., Chen, S.-J., Chang, P.-H., Lin, C.-J., Tai, M.-C., and Chou, Y.-B. (2022). Prediction of treatment outcome in neovascular age-related macular degeneration using a novel convolutional neural network. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-09642-7"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gan, F., Chen, W.-Y., Liu, H., and Zhong, Y.-L. (2022). Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.1084118"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7651462","DOI":"10.1155\/2021\/7651462","article-title":"Research on an Intelligent Lightweight-Assisted Pterygium Diagnosis Model Based on Anterior Segment Images","volume":"2021","author":"Zheng","year":"2021","journal-title":"Dis. Markers"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xu, W., Jin, L., Zhu, P.-Z., He, K., Yang, W.-H., and Wu, M.-N. (2021). Implementation and Application of an Intelligent Pterygium Diagnosis System Based on Deep Learning. Front. Psychol., 12.","DOI":"10.3389\/fpsyg.2021.759229"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e5738","DOI":"10.1002\/cpe.5738","article-title":"SERU: A cascaded SE-ResNeXT U-Net for kidney and tumor segmentation","volume":"32","author":"Xie","year":"2020","journal-title":"Concurr. Comput. Pr. Exp."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1007\/s10489-020-02010-w","article-title":"An automatic approach based on CNN architecture to detect COVID-19 disease from chest X-ray images","volume":"51","author":"Hira","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kim, M., Gil, Y., Kim, Y., and Kim, J. (2023). Deep-Learning-Based Scalp Image Analysis Using Limited Data. Electronics, 12.","DOI":"10.3390\/electronics12061380"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tariq, H., Rashid, M., Javed, A., Zafar, E., Alotaibi, S.S., and Zia, M.Y.I. (2022). Performance analysis of deep-neural-network-based automatic diagnosis of diabetic retinopathy. Sensors, 22.","DOI":"10.3390\/s22010205"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated Residual Transformations for Deep Neural Networks. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1361","DOI":"10.18240\/ijo.2023.09.02","article-title":"Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023)","volume":"16","author":"Yang","year":"2023","journal-title":"Int. J. Ophthalmol."},{"key":"ref_26","unstructured":"Tebes, G., Peppino, D., Becker, P., and Olsina, L. (2022, February 01). Especificaci\u00f3n del Modelo de Proceso para una Revisi\u00f3n Sistem\u00e1tica de Literatura. La Paz. 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