{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T06:29:54Z","timestamp":1772778594689,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T00:00:00Z","timestamp":1712102400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T00:00:00Z","timestamp":1712102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Bandirma Onyedi Eylul University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Glaucoma is an eye disease that spreads over time without showing any symptoms at an early age and can result in vision loss in advanced ages. The most critical issue in this disease is to detect the symptoms of the disease at an early age. Various researches are carried out on machine learning approaches that will provide support to the expert for this diagnosis. The activation function plays a pivotal role in deep learning models, as it introduces nonlinearity, enabling neural networks to learn complex patterns and relationships within data, thus facilitating accurate predictions and effective feature representations. In this study, it is focused on developing an activation function that can be used in CNN architectures using glaucoma disease datasets. The developed function (Trish) was compared with ReLU, LReLU, Mish, Swish, Smish, and Logish activation functions using SGD, Adam, RmsProp, AdaDelta, AdaGrad, Adamax, and Nadam optimizers in CNN architectures. Datasets consisting of retinal fundus images named ACRIMA and HRF were used within the scope of the experiments. These datasets are widely known and currently used in the literature. To strengthen the test validity, the proposed function was also tested on the CIFAR-10 dataset. As a result of the study, 97.22% validation accuracy performance was obtained. It should be stated that the acquired performance value is at a significant level for the detection of glaucoma.<\/jats:p>","DOI":"10.1007\/s11227-024-06057-1","type":"journal-article","created":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T18:02:14Z","timestamp":1712167334000},"page":"15485-15516","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Trish: an efficient activation function for CNN models and analysis of its effectiveness with optimizers in diagnosing glaucoma"],"prefix":"10.1007","volume":"80","author":[{"given":"Cemil","family":"K\u00f6zkurt","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aykut","family":"Diker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah","family":"Elen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Serhat","family":"K\u0131l\u0131\u00e7arslan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emrah","family":"D\u00f6nmez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fahrettin Burak","family":"Demir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"6057_CR1","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.ajo.2020.09.026","volume":"222","author":"K Nouri-Mahdavi","year":"2021","unstructured":"Nouri-Mahdavi K, Weiss RE (2021) Detection of glaucoma deterioration in the macular region with optical coherence tomography: challenges and solutions. Am J Ophthalmol 222:277\u2013284. https:\/\/doi.org\/10.1016\/j.ajo.2020.09.026","journal-title":"Am J Ophthalmol"},{"key":"6057_CR2","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.ajo.2020.12.031","volume":"225","author":"T Lee","year":"2021","unstructured":"Lee T, Jammal AA, Mariottoni EB, Medeiros FA (2021) Predicting glaucoma development with longitudinal deep learning predictions from fundus photographs. Am J Ophthalmol 225:86\u201394. https:\/\/doi.org\/10.1016\/j.ajo.2020.12.031","journal-title":"Am J Ophthalmol"},{"key":"6057_CR3","doi-asserted-by":"publisher","first-page":"104007","DOI":"10.1016\/j.ijmedinf.2019.104007","volume":"133","author":"SY Wang","year":"2020","unstructured":"Wang SY, Pershing S, Tran E, Hernandez-Boussard T (2020) Automated extraction of ophthalmic surgery outcomes from the electronic health record. Int J Med Inf 133:104007. https:\/\/doi.org\/10.1016\/j.ijmedinf.2019.104007","journal-title":"Int J Med Inf"},{"issue":"2","key":"6057_CR4","doi-asserted-by":"publisher","first-page":"100127","DOI":"10.1016\/j.xops.2022.100127","volume":"2","author":"SY Wang","year":"2022","unstructured":"Wang SY, Tseng B, Hernandez-Boussard T (2022) Deep learning approaches for predicting glaucoma progression using electronic health records and natural language processing. Ophthalmol Sci 2(2):100127. https:\/\/doi.org\/10.1016\/j.xops.2022.100127","journal-title":"Ophthalmol Sci"},{"issue":"4","key":"6057_CR5","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1136\/bjophthalmol-2019-315600","volume":"105","author":"Y Hashimoto","year":"2021","unstructured":"Hashimoto Y et al (2021) Deep learning model to predict visual field in central 10\u00b0 from optical coherence tomography measurement in glaucoma. Br J Ophthalmol 105(4):507\u2013513. https:\/\/doi.org\/10.1136\/bjophthalmol-2019-315600","journal-title":"Br J Ophthalmol"},{"key":"6057_CR6","doi-asserted-by":"publisher","first-page":"200149","DOI":"10.1016\/j.iswa.2022.200149","volume":"16","author":"I Iqbal","year":"2022","unstructured":"Iqbal I, Walayat K, Kakar MU, Ma J (2022) Automated identification of human gastrointestinal tract abnormalities based on deep convolutional neural network with endoscopic images. Intell Syst Appl 16:200149. https:\/\/doi.org\/10.1016\/j.iswa.2022.200149","journal-title":"Intell Syst Appl"},{"issue":"4","key":"6057_CR7","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.ogla.2020.04.012","volume":"3","author":"A Thakur","year":"2020","unstructured":"Thakur A, Goldbaum M, Yousefi S (2020) Predicting glaucoma before onset using deep learning. Ophthalmol Glaucoma 3(4):262\u2013268. https:\/\/doi.org\/10.1016\/j.ogla.2020.04.012","journal-title":"Ophthalmol Glaucoma"},{"key":"6057_CR8","doi-asserted-by":"crossref","unstructured":"Chayan TI, Islam A, Rahman E, Reza MT, Apon TS, Alam MGR (2022) Explainable AI based glaucoma detection using transfer learning and LIME. Preprint http:\/\/arxiv.org\/abs\/2210.03332 (Eri\u015fim 20 \u015eubat 2023)","DOI":"10.1109\/CSDE56538.2022.10089310"},{"issue":"11","key":"6057_CR9","doi-asserted-by":"publisher","first-page":"e157968","DOI":"10.1172\/JCI157968","volume":"132","author":"F Li","year":"2022","unstructured":"Li F et al (2022) A deep-learning system predicts glaucoma incidence and progression using retinal photographs. J Clin Invest 132(11):e157968. https:\/\/doi.org\/10.1172\/JCI157968","journal-title":"J Clin Invest"},{"key":"6057_CR10","doi-asserted-by":"publisher","first-page":"101843","DOI":"10.1016\/j.compmedimag.2020.101843","volume":"88","author":"I Iqbal","year":"2021","unstructured":"Iqbal I, Younus M, Walayat K, Kakar MU, Ma J (2021) Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images. Comput Med Imaging Graph 88:101843. https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101843","journal-title":"Comput Med Imaging Graph"},{"issue":"4","key":"6057_CR11","doi-asserted-by":"publisher","first-page":"1618","DOI":"10.1177\/1120672120977346","volume":"31","author":"D Mirzania","year":"2021","unstructured":"Mirzania D, Thompson AC, Muir KW (2021) Applications of deep learning in detection of glaucoma: a systematic review. Eur J Ophthalmol 31(4):1618\u20131642. https:\/\/doi.org\/10.1177\/1120672120977346","journal-title":"Eur J Ophthalmol"},{"key":"6057_CR12","doi-asserted-by":"publisher","DOI":"10.1111\/aos.14193","author":"R Hemelings","year":"2020","unstructured":"Hemelings R et al (2020) Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning. Acta Ophthalmol. https:\/\/doi.org\/10.1111\/aos.14193","journal-title":"Acta Ophthalmol"},{"issue":"3","key":"6057_CR13","doi-asserted-by":"publisher","first-page":"510","DOI":"10.3390\/diagnostics11030510","volume":"11","author":"S Oh","year":"2021","unstructured":"Oh S, Park Y, Cho KJ, Kim SJ (2021) Explainable machine learning model for glaucoma diagnosis and its interpretation. Diagnostics 11(3):510. https:\/\/doi.org\/10.3390\/diagnostics11030510","journal-title":"Diagnostics"},{"key":"6057_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3171613","volume":"71","author":"MS Kamal","year":"2022","unstructured":"Kamal MS, Dey N, Chowdhury L, Hasan SI, Santosh K (2022) Explainable AI for glaucoma prediction analysis to understand risk factors in treatment planning. IEEE Trans Instrum Meas 71:1\u20139. https:\/\/doi.org\/10.1109\/TIM.2022.3171613","journal-title":"IEEE Trans Instrum Meas"},{"key":"6057_CR15","doi-asserted-by":"publisher","first-page":"923096","DOI":"10.3389\/fmed.2022.923096","volume":"9","author":"X Huang","year":"2022","unstructured":"Huang X et al (2022) Detecting glaucoma from multi-modal data using probabilistic deep learning. Front Med 9:923096. https:\/\/doi.org\/10.3389\/fmed.2022.923096","journal-title":"Front Med"},{"key":"6057_CR16","doi-asserted-by":"publisher","first-page":"937205","DOI":"10.3389\/fopht.2022.937205","volume":"2","author":"AC Thompson","year":"2022","unstructured":"Thompson AC, Falconi A, Sappington RM (2022) Deep learning and optical coherence tomography in glaucoma: bridging the diagnostic gap on structural imaging. Front Ophthalmol 2:937205. https:\/\/doi.org\/10.3389\/fopht.2022.937205","journal-title":"Front Ophthalmol"},{"key":"6057_CR17","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/bs.pbr.2020.07.002","volume":"257","author":"MJA Girard","year":"2020","unstructured":"Girard MJA, Schmetterer L (2020) Artificial intelligence and deep learning in glaucoma: current state and future prospects. Progress Brain Res 257:37\u201364. https:\/\/doi.org\/10.1016\/bs.pbr.2020.07.002","journal-title":"Progress Brain Res"},{"issue":"10","key":"6057_CR18","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.3390\/healthcare10101831","volume":"10","author":"F Tarcoveanu","year":"1831","unstructured":"Tarcoveanu F, Leon F, Curteanu S, Chiselita D, Bogdanici CM, Anton N (1831) Classification algorithms used in predicting glaucoma progression. Healthcare 10(10):2022. https:\/\/doi.org\/10.3390\/healthcare10101831","journal-title":"Healthcare"},{"key":"6057_CR19","doi-asserted-by":"publisher","unstructured":"Madadi Y, Abu-Serhan H, Yousefi S (2022) Domain adaptation-based deep learning models for forecasting and diagnosis of glaucoma disease. https:\/\/doi.org\/10.36227\/techrxiv.21391551.v2","DOI":"10.36227\/techrxiv.21391551.v2"},{"issue":"11","key":"6057_CR20","doi-asserted-by":"publisher","first-page":"810","DOI":"10.3390\/photonics9110810","volume":"9","author":"R Nunez","year":"2022","unstructured":"Nunez R et al (2022) Artificial intelligence to aid glaucoma diagnosis and monitoring: state of the art and new directions. Photonics 9(11):810. https:\/\/doi.org\/10.3390\/photonics9110810","journal-title":"Photonics"},{"issue":"6","key":"6057_CR21","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1007\/s12530-022-09426-4","volume":"13","author":"LK Singh","year":"2022","unstructured":"Singh LK, Pooja, Garg H, Khanna M (2022) Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets. Evol Syst 13(6):807\u2013836. https:\/\/doi.org\/10.1007\/s12530-022-09426-4","journal-title":"Evol Syst"},{"issue":"1","key":"6057_CR22","doi-asserted-by":"publisher","first-page":"8064","DOI":"10.1038\/s41598-022-12147-y","volume":"12","author":"N Akter","year":"2022","unstructured":"Akter N, Fletcher J, Perry S, Simunovic MP, Briggs N, Roy M (2022) Glaucoma diagnosis using multi-feature analysis and a deep learning technique. Sci Rep 12(1):8064. https:\/\/doi.org\/10.1038\/s41598-022-12147-y","journal-title":"Sci Rep"},{"key":"6057_CR23","doi-asserted-by":"publisher","unstructured":"Pham QTM, Han JC, Shin J (2022) A multimodal deep learning model for predicting future visual field in glaucoma patients. In: Review. https:\/\/doi.org\/10.21203\/rs.3.rs-1236761\/v1","DOI":"10.21203\/rs.3.rs-1236761\/v1"},{"issue":"1","key":"6057_CR24","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12938-019-0649-y","volume":"18","author":"A Diaz-Pinto","year":"2019","unstructured":"Diaz-Pinto A, Morales S, Naranjo V, K\u00f6hler T, Mossi JM, Navea A (2019) CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomed Eng Online 18(1):29. https:\/\/doi.org\/10.1186\/s12938-019-0649-y","journal-title":"Biomed Eng Online"},{"key":"6057_CR25","doi-asserted-by":"publisher","first-page":"e154860","DOI":"10.1155\/2013\/154860","volume":"2013","author":"A Budai","year":"2013","unstructured":"Budai A, Bock R, Maier A, Hornegger J, Michelson G (2013) Robust vessel segmentation in fundus images. Int J Biomed Imaging 2013:e154860. https:\/\/doi.org\/10.1155\/2013\/154860","journal-title":"Int J Biomed Imaging"},{"issue":"6","key":"6057_CR26","doi-asserted-by":"publisher","first-page":"2259","DOI":"10.1002\/jemt.24083","volume":"85","author":"S Akbar","year":"2022","unstructured":"Akbar S, Hassan SA, Shoukat A, Alyami J, Bahaj SA (2022) Detection of microscopic glaucoma through fundus images using deep transfer learning approach. Microsc Res Tech 85(6):2259\u20132276. https:\/\/doi.org\/10.1002\/jemt.24083","journal-title":"Microsc Res Tech"},{"issue":"7553","key":"6057_CR27","doi-asserted-by":"publisher","first-page":"7553","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):7553. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"6057_CR28","doi-asserted-by":"publisher","first-page":"114805","DOI":"10.1016\/j.eswa.2021.114805","volume":"174","author":"S Kili\u00e7arslan","year":"2021","unstructured":"Kili\u00e7arslan S, Celik M (2021) RSigELU: a nonlinear activation function for deep neural networks. Expert Syst Appl 174:114805. https:\/\/doi.org\/10.1016\/j.eswa.2021.114805","journal-title":"Expert Syst Appl"},{"issue":"11","key":"6057_CR29","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc IEEE"},{"issue":"10","key":"6057_CR30","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1080\/08839514.2021.1922841","volume":"35","author":"I Iqbal","year":"2021","unstructured":"Iqbal I, Odesanmi GA, Wang J, Liu L (2021) Comparative investigation of learning algorithms for image classification with small dataset. Appl Artif Intell 35(10):697\u2013716. https:\/\/doi.org\/10.1080\/08839514.2021.1922841","journal-title":"Appl Artif Intell"},{"key":"6057_CR31","doi-asserted-by":"publisher","first-page":"119503","DOI":"10.1016\/j.eswa.2023.119503","volume":"217","author":"S Kili\u00e7arslan","year":"2023","unstructured":"Kili\u00e7arslan S, K\u00f6zkurt C, Ba\u015f S, Elen A (2023) Detection and classification of pneumonia using novel superior exponential (SupEx) activation function in convolutional neural networks. Expert Syst Appl 217:119503. https:\/\/doi.org\/10.1016\/j.eswa.2023.119503","journal-title":"Expert Syst Appl"},{"key":"6057_CR32","doi-asserted-by":"publisher","unstructured":"Ramachandran P, Zoph B, Le QV (2017) Searching for activation functions. https:\/\/doi.org\/10.48550\/arXiv.1710.05941","DOI":"10.48550\/arXiv.1710.05941"},{"key":"6057_CR33","unstructured":"Misra D (2020) Mish: a self regularized non-monotonic activation function. Preprint http:\/\/arxiv.org\/abs\/1908.08681"},{"key":"6057_CR34","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1016\/j.neucom.2021.06.067","volume":"458","author":"H Zhu","year":"2021","unstructured":"Zhu H, Zeng H, Liu J, Zhang X (2021) Logish: a new nonlinear nonmonotonic activation function for convolutional neural network. Neurocomputing 458:490\u2013499. https:\/\/doi.org\/10.1016\/j.neucom.2021.06.067","journal-title":"Neurocomputing"},{"key":"6057_CR35","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11040540","author":"X Wang","year":"2022","unstructured":"Wang X, Ren H, Wang A (2022) Smish: a novel activation function for deep learning methods. Electronics. https:\/\/doi.org\/10.3390\/electronics11040540","journal-title":"Electronics"},{"key":"6057_CR36","doi-asserted-by":"crossref","unstructured":"Weinreb RN, Aung T, Medeiros FA (2014) The pathophysiology and treatment of glaucoma: a review. Clin Rev Educ Am Med Assoc","DOI":"10.1001\/jama.2014.3192"},{"key":"6057_CR37","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Valverde JJ, Ant\u00f3n A, Fatti G, Liefers B, Herranz A, Santos A, S\u00e1nchez CI, Ledesma-Carbayo MJ (2019) Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomed Opt Express 10","DOI":"10.1364\/BOE.10.000892"},{"key":"6057_CR38","doi-asserted-by":"crossref","unstructured":"Juneja M, Thakur S, Uniyal A, Wani A, Thakur N, Jindal P (2022) Deep learning-based classification network for glaucoma in retinal images. Comput Electr Eng 101","DOI":"10.1016\/j.compeleceng.2022.108009"},{"key":"6057_CR39","doi-asserted-by":"crossref","unstructured":"Balasubramanian K, Ramya K, Devi KG (2022) Improved swarm optimization of deep features for glaucoma classification using SEGSO and VGGNet. Biomed Signal Process Control 77","DOI":"10.1016\/j.bspc.2022.103845"},{"key":"6057_CR40","doi-asserted-by":"crossref","unstructured":"Claro M, Veras R, Santana A, Araujo F, Silva R, Almeida J, Leite D (2019) An hybrid feature space from texture information and transfer learning for glaucoma classification. J Vis Commun Image R 64","DOI":"10.1016\/j.jvcir.2019.102597"},{"key":"6057_CR41","doi-asserted-by":"publisher","first-page":"115211","DOI":"10.1016\/j.eswa.2021.115211","volume":"182","author":"TJ Jun","year":"2021","unstructured":"Jun TJ, Eom Y, Kim D, Kim C, Park JH, Nguyen HM, Kim YH, Kim D (2021) TRk-CNN: transferable ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes. Expert Syst Appl 182:115211","journal-title":"Expert Syst Appl"},{"key":"6057_CR42","doi-asserted-by":"crossref","unstructured":"Haider A, Arsalan M, Lee MB, Owais M, Mahmood T, Sultan H, Park KR (2022) Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images. Expert Syst Appl 207","DOI":"10.1016\/j.eswa.2022.117968"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06057-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06057-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06057-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T11:26:28Z","timestamp":1719314788000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06057-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,3]]},"references-count":42,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["6057"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06057-1","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,3]]},"assertion":[{"value":"8 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}