{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:39:11Z","timestamp":1781019551379,"version":"3.54.1"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Ministry of education in Saudi Arabia","award":["IFP-2022-22"],"award-info":[{"award-number":["IFP-2022-22"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-08249-y","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T16:39:24Z","timestamp":1752165564000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Autoimmune gastritis detection from preprocessed endoscopy images using deep transfer learning and moth flame optimization"],"prefix":"10.1038","volume":"15","author":[{"given":"Fadiyah M.","family":"Almutairi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara A.","family":"Althubiti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shabnam Mohamed","family":"Aslam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Habib","family":"Dhahri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Alhajlah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nitin","family":"Mittal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"key":"8249_CR1","doi-asserted-by":"crossref","unstructured":"Matsumoto, S. et al. Risk of map-like redness development after eradication therapy for Helicobacter pylori infection. Helicobacter 29(1), e13046 (2024).","DOI":"10.1111\/hel.13046"},{"issue":"3","key":"8249_CR2","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.cmpb.2015.09.005","volume":"122","author":"AR Hassan","year":"2015","unstructured":"Hassan, A. R. & Haque, M. A. Computer-aided Gastrointestinal haemorrhage detection in wireless capsule endoscopy videos. Comput. Methods Programs Biomed. 122 (3), 341\u2013353 (2015).","journal-title":"Comput. Methods Programs Biomed."},{"key":"8249_CR3","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.cmpb.2018.01.013","volume":"157","author":"H Ali","year":"2018","unstructured":"Ali, H., Yasmin, M., Sharif, M. & Rehmani, M. H. Computer-assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images. Comput. Methods Programs Biomed. 157, 39\u201347 (2018).","journal-title":"Comput. Methods Programs Biomed."},{"issue":"10","key":"8249_CR4","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.2174\/1573405616666200425220513","volume":"16","author":"A Liaqat","year":"2020","unstructured":"Liaqat, A. et al. Gastric tract infections detection and classification from wireless capsule endoscopy using computer vision techniques: A review. Curr. Med. Imaging. 16 (10), 1229\u20131242 (2020).","journal-title":"Curr. Med. Imaging"},{"key":"8249_CR5","doi-asserted-by":"publisher","first-page":"132850","DOI":"10.1109\/ACCESS.2020.3010448","volume":"8","author":"MA Khan","year":"2020","unstructured":"Khan, M. A. et al. Computer-aided Gastrointestinal diseases analysis from wireless capsule endoscopy: a framework of best features selection. IEEE Access. 8, 132850\u2013132859 (2020).","journal-title":"IEEE Access."},{"key":"8249_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1203-y","volume":"43","author":"A Yasar","year":"2019","unstructured":"Yasar, A., Saritas, I. & Korkmaz, H. Computer-aided diagnosis system for the detection of stomach cancer with image processing techniques. J. Med. Syst. 43, 1\u201311 (2019).","journal-title":"J. Med. Syst."},{"key":"8249_CR7","doi-asserted-by":"publisher","first-page":"175628482110177","DOI":"10.1177\/17562848211017730","volume":"14","author":"GE Tontini","year":"2021","unstructured":"Tontini, G. E. et al. Artificial intelligence in Gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons. Therapeutic Adv. Gastroenterol. 14, 17562848211017730 (2021).","journal-title":"Therapeutic Adv. Gastroenterol."},{"issue":"12","key":"8249_CR8","doi-asserted-by":"publisher","first-page":"3523","DOI":"10.3390\/jcm11123523","volume":"11","author":"M Kishino","year":"2022","unstructured":"Kishino, M. & Nonaka, K. Endoscopic features of autoimmune gastritis: focus on typical images and early images. J. Clin. Med. 11 (12), 3523 (2022).","journal-title":"J. Clin. Med."},{"issue":"6","key":"8249_CR9","doi-asserted-by":"publisher","first-page":"827","DOI":"10.2169\/internalmedicine.8178-21","volume":"61","author":"M Ayaki","year":"2022","unstructured":"Ayaki, M., Manabe, N., Murota, M., Fujita, M. & Haruma, K. An evaluation of endoscopic images from over 15 years before the diagnosis of autoimmune gastritis: a report of three patients. Intern. Med. 61 (6), 827\u2013833 (2022).","journal-title":"Intern. Med."},{"key":"8249_CR10","doi-asserted-by":"publisher","first-page":"Article0138","DOI":"10.34133\/cbsystems.0138","volume":"5","author":"B Wang","year":"2024","unstructured":"Wang, B. et al. Low-Friction soft robots for targeted bacterial infection treatment in Gastrointestinal tract. Cyborg Bionic Syst. 5, Article0138. https:\/\/doi.org\/10.34133\/cbsystems.0138 (2024).","journal-title":"Cyborg Bionic Syst."},{"key":"8249_CR11","doi-asserted-by":"publisher","first-page":"108080","DOI":"10.1016\/j.cmpb.2024.108080","volume":"247","author":"H Li","year":"2024","unstructured":"Li, H. et al. UCFNNet: ulcerative colitis evaluation based on fine-grained lesion learner and noise suppression gating. Comput. Methods Programs Biomed. 247, 108080. https:\/\/doi.org\/10.1016\/j.cmpb.2024.108080 (2024).","journal-title":"Comput. Methods Programs Biomed."},{"issue":"19","key":"8249_CR12","doi-asserted-by":"publisher","first-page":"33378","DOI":"10.1364\/OE.530764","volume":"32","author":"N Ma","year":"2024","unstructured":"Ma, N. et al. Enhancing the sensitivity of spin-exchange relaxation-free magnetometers using phase-modulated pump light with external Gaussian noise. Opt. Express. 32 (19), 33378\u201333390. https:\/\/doi.org\/10.1364\/OE.530764 (2024).","journal-title":"Opt. Express"},{"issue":"3","key":"8249_CR13","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1111\/den.13500","volume":"32","author":"S Terao","year":"2020","unstructured":"Terao, S. et al. Multicenter study of autoimmune gastritis in japan: clinical and endoscopic characteristics. Dig. Endoscopy. 32 (3), 364\u2013372 (2020).","journal-title":"Dig. Endoscopy"},{"issue":"1","key":"8249_CR14","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s41572-020-0187-8","volume":"6","author":"MV Lenti","year":"2020","unstructured":"Lenti, M. V. et al. Autoimmune gastritis. Nat. Reviews Disease Primers. 6 (1), 56 (2020).","journal-title":"Nat. Reviews Disease Primers"},{"issue":"4","key":"8249_CR15","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1053\/j.gastro.2021.06.078","volume":"161","author":"SC Shah","year":"2021","unstructured":"Shah, S. C., Piazuelo, M. B., Kuipers, E. J. & Li, D. AGA clinical practice update on the diagnosis and management of atrophic gastritis: expert review. Gastroenterology 161 (4), 1325\u20131332 (2021).","journal-title":"Gastroenterology"},{"issue":"4","key":"8249_CR16","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1111\/den.14175","volume":"34","author":"T Kamada","year":"2022","unstructured":"Kamada, T., Maruyama, Y., Monobe, Y. & Haruma, K. Endoscopic features and clinical importance of autoimmune gastritis. Dig. Endoscopy. 34 (4), 700\u2013713 (2022).","journal-title":"Dig. Endoscopy"},{"issue":"1","key":"8249_CR17","doi-asserted-by":"publisher","first-page":"4202","DOI":"10.1038\/s41598-022-07947-1","volume":"12","author":"H Kishikawa","year":"2022","unstructured":"Kishikawa, H. et al. Relevance of pepsinogen, gastrin, and endoscopic atrophy in the diagnosis of autoimmune gastritis. Sci. Rep. 12 (1), 4202 (2022).","journal-title":"Sci. Rep."},{"issue":"3","key":"8249_CR18","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/s12328-022-01617-5","volume":"15","author":"T Kotera","year":"2022","unstructured":"Kotera, T., Yamanishi, M., Kushima, R. & Haruma, K. Early autoimmune gastritis presenting with a normal endoscopic appearance. Clin. J. Gastroenterol. 15 (3), 547\u2013552 (2022).","journal-title":"Clin. J. Gastroenterol."},{"issue":"3","key":"8249_CR19","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s00535-022-01954-9","volume":"58","author":"T Kamada","year":"2023","unstructured":"Kamada, T. et al. Diagnostic criteria and endoscopic and histological findings of autoimmune gastritis in Japan. J. Gastroenterol. 58 (3), 185\u2013195 (2023).","journal-title":"J. Gastroenterol."},{"key":"8249_CR20","doi-asserted-by":"crossref","unstructured":"Massironi, S. et al. Occurrence and characteristics of endoscopic gastric polyps in patients with autoimmune gastritis (AGAPE study): A multicentric cross-sectional study. Digest. Liver Dis.  (2024).","DOI":"10.1016\/j.dld.2024.07.024"},{"key":"8249_CR21","doi-asserted-by":"crossref","unstructured":"Chen, S. et al. A novel endoscopic artificial intelligence system to assist in the diagnosis of autoimmune gastritis: A multicenter study. Endoscopy (2024).","DOI":"10.1055\/a-2451-3071"},{"issue":"24","key":"8249_CR22","doi-asserted-by":"publisher","first-page":"245023","DOI":"10.1088\/1361-6560\/ad0a5a","volume":"68","author":"S Luan","year":"2023","unstructured":"Luan, S. et al. Deep learning for fast super-resolution ultrasound microvessel imaging. Phys. Med. Biol. 68 (24), 245023. https:\/\/doi.org\/10.1088\/1361-6560\/ad0a5a (2023).","journal-title":"Phys. Med. Biol."},{"issue":"4","key":"8249_CR23","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1007\/s40120-024-00642-5","volume":"13","author":"L Wang","year":"2024","unstructured":"Wang, L. et al. Neuroendoscopic parafascicular evacuation of spontaneous intracerebral hemorrhage (NESICH Technique): A Multicenter Technical Experience with Preliminary Findings. Neurol. Therapy. 13 (4), 1259\u20131271. https:\/\/doi.org\/10.1007\/s40120-024-00642-5 (2024).","journal-title":"Neurol. Therapy"},{"key":"8249_CR24","doi-asserted-by":"crossref","unstructured":"Jamil, D., Palaniappan, S., Lokman, A., Naseem, M. & Zia, S. S. Diagnosis of gastric cancer using machine learning techniques in healthcare sector: A survey. Informatica, 45(7). (2022).","DOI":"10.31449\/inf.v45i7.3633"},{"key":"8249_CR25","first-page":"4087","volume":"70","author":"V Rajinikanth","year":"2022","unstructured":"Rajinikanth, V., Aslam, S. M., Kadry, S. & Thinnukool, O. Semi\/fully-automated segmentation of gastric-polyp using aquila-optimization-algorithm enhanced images. CMC-Comput Mater. Contin. 70, 4087\u20134105 (2022).","journal-title":"CMC-Comput Mater. Contin"},{"key":"8249_CR26","doi-asserted-by":"publisher","first-page":"1432869","DOI":"10.3389\/fonc.2024.1432869","volume":"14","author":"S Chen","year":"2024","unstructured":"Chen, S. et al. Evaluation of a three-gene methylation model for correlating lymph node metastasis in postoperative early gastric cancer adjacent samples. Front. Oncol. 14, 1432869. https:\/\/doi.org\/10.3389\/fonc.2024.1432869 (2024).","journal-title":"Front. Oncol."},{"key":"8249_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3331425","volume":"73","author":"T Long","year":"2024","unstructured":"Long, T., Song, X., Han, B., Suo, Y. & Jia, L. In situ magnetic field compensation method for optically pumped magnetometers under Three-Axis nonorthogonality. IEEE Trans. Instrum. Meas. 73, 1\u201312. https:\/\/doi.org\/10.1109\/TIM.2023.3331425 (2024).","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"2","key":"8249_CR28","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1007\/s40120-021-00279-8","volume":"10","author":"C Zhang","year":"2021","unstructured":"Zhang, C. et al. Hematoma evacuation via Image-Guided Para-Corticospinal tract approach in patients with spontaneous intracerebral hemorrhage. Neurol. Therapy. 10 (2), 1001\u20131013. https:\/\/doi.org\/10.1007\/s40120-021-00279-8 (2021).","journal-title":"Neurol. Therapy"},{"issue":"6","key":"8249_CR29","doi-asserted-by":"publisher","first-page":"230212","DOI":"10.29026\/oea.2024.230212","volume":"7","author":"X Xu","year":"2024","unstructured":"Xu, X. et al. Large-field objective lens for multi-wavelength microscopy at mesoscale and submicron resolution. Opto-Electronic Adv. 7 (6), 230212. https:\/\/doi.org\/10.29026\/oea.2024.230212 (2024).","journal-title":"Opto-Electronic Adv."},{"key":"8249_CR30","doi-asserted-by":"publisher","DOI":"10.21227\/3yqx-d789","author":"L Dawei Gong","year":"2023","unstructured":"Dawei Gong, L., BinbinGu, Y., Zhang, R., Mao, X. & Sailing He. Gastritis 2 classification dataset. IEEE Dataport. https:\/\/doi.org\/10.21227\/3yqx-d789 (2023).","journal-title":"IEEE Dataport"},{"key":"8249_CR31","doi-asserted-by":"crossref","unstructured":"Rajinikanth, V. et al. COVID-19 detection in lung CT slices using Brownian-butterfly-algorithm optimized lightweight deep features. Heliyon 10(5). (2024).","DOI":"10.1016\/j.heliyon.2024.e27509"},{"issue":"13","key":"8249_CR32","doi-asserted-by":"publisher","first-page":"19335","DOI":"10.1007\/s11042-021-10641-5","volume":"80","author":"S Mahajan","year":"2021","unstructured":"Mahajan, S., Mittal, N. & Pandit, A. K. Image segmentation using multilevel thresholding based on type II fuzzy entropy and marine predators algorithm. Multimedia Tools Appl. 80 (13), 19335\u201319359 (2021).","journal-title":"Multimedia Tools Appl."},{"issue":"6","key":"8249_CR33","doi-asserted-by":"publisher","first-page":"8537","DOI":"10.1007\/s11042-022-13551-2","volume":"82","author":"S Mahajan","year":"2023","unstructured":"Mahajan, S., Mittal, N. & Pandit, A. K. Image segmentation approach based on adaptive flower pollination algorithm and type II fuzzy entropy. Multimedia Tools Appl. 82 (6), 8537\u20138559 (2023).","journal-title":"Multimedia Tools Appl."},{"key":"8249_CR34","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/j.neucom.2018.09.034","volume":"321","author":"S Hinojosa","year":"2018","unstructured":"Hinojosa, S., Dhal, K. G., Abd Elaziz, M., Oliva, D. & Cuevas, E. Entropy-based imagery segmentation for breast histology using the stochastic fractal search. Neurocomputing 321, 201\u2013215 (2018).","journal-title":"Neurocomputing"},{"key":"8249_CR35","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1016\/j.procs.2023.01.439","volume":"219","author":"S Jardim","year":"2023","unstructured":"Jardim, S., Ant\u00f3nio, J. & Mora, C. Image thresholding approaches for medical image segmentation-short literature review. Procedia Comput. Sci. 219, 1485\u20131492 (2023).","journal-title":"Procedia Comput. Sci."},{"key":"8249_CR36","doi-asserted-by":"publisher","first-page":"105522","DOI":"10.1016\/j.asoc.2019.105522","volume":"97","author":"P Upadhyay","year":"2020","unstructured":"Upadhyay, P. & Chhabra, J. K. Kapur\u2019s entropy based optimal multilevel image segmentation using crow search algorithm. Appl. Soft Comput. 97, 105522 (2020).","journal-title":"Appl. Soft Comput."},{"issue":"2\u20133","key":"8249_CR37","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1504\/IJCSYSE.2018.091395","volume":"4","author":"T Kaur","year":"2018","unstructured":"Kaur, T., Saini, B. S. & Gupta, S. A comparative study on kapur\u2019s and Tsallis entropy for multilevel thresholding of MR images via particle swarm optimisation technique. Int. J. Comput. Syst. Eng. 4 (2\u20133), 156\u2013164 (2018).","journal-title":"Int. J. Comput. Syst. Eng."},{"issue":"3","key":"8249_CR38","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1016\/j.aej.2017.05.024","volume":"57","author":"MSR Naidu","year":"2018","unstructured":"Naidu, M. S. R., Kumar, P. R. & Chiranjeevi, K. Shannon and fuzzy entropy based evolutionary image thresholding for image segmentation. Alexandria Eng. J. 57 (3), 1643\u20131655 (2018).","journal-title":"Alexandria Eng. J."},{"key":"8249_CR39","doi-asserted-by":"publisher","first-page":"171218","DOI":"10.1109\/ACCESS.2020.3024718","volume":"8","author":"Y Zou","year":"2020","unstructured":"Zou, Y., Zhang, J., Upadhyay, M., Sun, S. & Jiang, T. Automatic image thresholding based on Shannon entropy difference and dynamic synergic entropy. Ieee Access. 8, 171218\u2013171239 (2020).","journal-title":"Ieee Access."},{"issue":"6","key":"8249_CR40","first-page":"3247","volume":"24","author":"A Elaraby","year":"2017","unstructured":"Elaraby, A. & Moratal, D. A generalized entropy-based two-phase threshold algorithm for noisy medical image edge detection. Scientia Iranica. 24 (6), 3247\u20133256 (2017).","journal-title":"Scientia Iranica"},{"key":"8249_CR41","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.asoc.2016.10.032","volume":"50","author":"S Sarkar","year":"2017","unstructured":"Sarkar, S., Das, S. & Chaudhuri, S. S. Multi-level thresholding with a decomposition-based multi-objective evolutionary algorithm for segmenting natural and medical images. Appl. Soft Comput. 50, 142\u2013157 (2017).","journal-title":"Appl. Soft Comput."},{"key":"8249_CR42","doi-asserted-by":"crossref","unstructured":"Mahmoudi, L. & El Zaart, A. A survey of entropy image thresholding techniques. In 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), 204\u2013209. IEEE. (2012).","DOI":"10.1109\/ICTEA.2012.6462867"},{"issue":"6","key":"8249_CR43","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1049\/ip-vis:20050032","volume":"153","author":"CI Chang","year":"2006","unstructured":"Chang, C. I., Du, Y., Wang, J., Guo, S. M. & Thouin, P. D. Survey and comparative analysis of entropy and relative entropy thresholding techniques. IEE Proceedings-Vision Image Signal. Process. 153 (6), 837\u2013850 (2006).","journal-title":"IEE Proceedings-Vision Image Signal. Process."},{"issue":"2","key":"8249_CR44","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1080\/09720502.2020.1731976","volume":"23","author":"A Sharma","year":"2020","unstructured":"Sharma, A., Chaturvedi, R., Kumar, S. & Dwivedi, U. K. Multi-level image thresholding based on Kapur and Tsallis entropy using firefly algorithm. J. Interdisciplinary Math. 23 (2), 563\u2013571 (2020).","journal-title":"J. Interdisciplinary Math."},{"issue":"5","key":"8249_CR45","first-page":"391","volume":"2","author":"V Gautam","year":"2013","unstructured":"Gautam, V. & Lakhwani, K. Implementation of Non Shannon entropy measures for color image segmentation and comparison with Shannon entropy measures. Int. J. Sci. Res. (Ahmedabad India). 2 (5), 391\u2013394 (2013).","journal-title":"Int. J. Sci. Res. (Ahmedabad India)"},{"key":"8249_CR46","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.knosys.2015.07.006","volume":"89","author":"S Mirjalili","year":"2015","unstructured":"Mirjalili, S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89, 228\u2013249 (2015).","journal-title":"Knowl. Based Syst."},{"key":"8249_CR47","doi-asserted-by":"publisher","unstructured":"Rajinikanth, V., Kadry, S., Gonz\u00e1lez-Crespo, R. & Verd\u00fa, E. A study on RGB image multi-thresholding using kapur\/tsallis entropy and moth-flame algorithm. https:\/\/doi.org\/10.9781\/ijimai.2021.11.008  (2021).","DOI":"10.9781\/ijimai.2021.11.008"},{"key":"8249_CR48","doi-asserted-by":"crossref","unstructured":"Muangkote, N., Sunat, K. & Chiewchanwattana, S. Multilevel thresholding for satellite image segmentation with moth-flame based optimization. In 2016 13th international joint conference on computer science and software engineering (JCSSE) (pp. 1\u20136). IEEE. (2016), July.","DOI":"10.1109\/JCSSE.2016.7748919"},{"issue":"4","key":"8249_CR49","doi-asserted-by":"publisher","first-page":"58","DOI":"10.4018\/IJAMC.2017100104","volume":"8","author":"AKM Khairuzzaman","year":"2017","unstructured":"Khairuzzaman, A. K. M. & Chaudhury, S. Moth-flame optimization algorithm based multilevel thresholding for image segmentation. Int. J. Appl. Metaheuristic Comput. (IJAMC). 8 (4), 58\u201383 (2017).","journal-title":"Int. J. Appl. Metaheuristic Comput. (IJAMC)"},{"key":"8249_CR50","doi-asserted-by":"crossref","unstructured":"Al-Tashi, Q. et al. Moth-flame optimization algorithm for feature selection: A review and future trends. Handbook Moth-Flame Optim. Algorithm, 11\u201334. (2022).","DOI":"10.1201\/9781003205326-3"},{"key":"8249_CR51","doi-asserted-by":"publisher","first-page":"8041","DOI":"10.1109\/ACCESS.2020.2964321","volume":"8","author":"I Tumar","year":"2020","unstructured":"Tumar, I., Hassouneh, Y., Turabieh, H. & Thaher, T. Enhanced binary moth flame optimization as a feature selection algorithm to predict software fault prediction. Ieee Access. 8, 8041\u20138055 (2020).","journal-title":"Ieee Access."},{"key":"8249_CR52","doi-asserted-by":"crossref","unstructured":"Khurma, R. A., Aljarah, I. & Sharieh, A. Rank based moth flame optimisation for feature selection in the medical application. In 2020 IEEE Congress on Evolutionary Computation (CEC),  1\u20138. (IEEE, 2020).","DOI":"10.1109\/CEC48606.2020.9185498"},{"key":"8249_CR53","doi-asserted-by":"crossref","unstructured":"Umapathy, S., Alhajlah, O., Almutairi, F., Aslam, S. & RK, A. F-Net: follicles net an efficient tool for the diagnosis of polycystic ovarian syndrome using deep learning techniques. Plos One 19(8), e0307571. (2024).","DOI":"10.1371\/journal.pone.0307571"},{"key":"8249_CR54","doi-asserted-by":"crossref","unstructured":"Du, W., Rao, N., Liu, D., Jiang, H., Luo, C., Li, Z. & Zeng, B. (2019). Review on the applications of deep learning in the analysis of gastrointestinal endoscopy images.IEEE Access 7, 142053\u2013142069.","DOI":"10.1109\/ACCESS.2019.2944676"},{"key":"8249_CR55","doi-asserted-by":"publisher","first-page":"94306","DOI":"10.1109\/ACCESS.2023.3311346","volume":"11","author":"VR Sankari","year":"2023","unstructured":"Sankari, V. R., Umapathy, U., Alasmari, S. & Aslam, S. M. Automated detection of retinopathy of prematurity using quantum machine learning and deep learning techniques. IEEE Access. 11, 94306\u201394321 (2023).","journal-title":"IEEE Access."},{"key":"8249_CR56","unstructured":"https:\/\/keras.io\/api\/applications\/."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-08249-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-08249-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-08249-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T04:47:00Z","timestamp":1757220420000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-08249-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,10]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["8249"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-08249-y","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,10]]},"assertion":[{"value":"2 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2025","order":3,"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":"Competing interests"}}],"article-number":"24940"}}