{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T01:13:25Z","timestamp":1776215605662,"version":"3.50.1"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100011644","name":"Kahramanmara\u015f S\u00fct\u00e7\u00fc Imam \u00dcniversitesi","doi-asserted-by":"publisher","award":["2023\/4-6 D"],"award-info":[{"award-number":["2023\/4-6 D"]}],"id":[{"id":"10.13039\/501100011644","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01137-3","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T21:01:26Z","timestamp":1727384486000},"page":"1846-1859","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MR Image Fusion-Based Parotid Gland Tumor Detection"],"prefix":"10.1007","volume":"38","author":[{"given":"Kubilay Muhammed","family":"Sunnetci","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esat","family":"Kaba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fatma Beyazal","family":"Celiker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmet","family":"Alkan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"issue":"654","key":"1137_CR1","volume":"2024","author":"M Bello","year":"2023","unstructured":"Bello M, N\u00e1poles G, Concepci\u00f3n L, Bello R, Mesejo P, Cord\u00f3n \u00d3. REPROT: Explaining the predictions of complex deep learning architectures for object detection through reducts of an image. Information Sciences. 2024;654(November 2023):119851.","journal-title":"Information Sciences."},{"issue":"128","key":"1137_CR2","volume":"2024","author":"M Sileo","year":"2023","unstructured":"Sileo M, Capece N, Gruosso M, Nigro M, Bloisi DD, Pierri F, et al. Vision-enhanced Peg-in-Hole for automotive body parts using semantic image segmentation and object detection. Engineering Applications of Artificial Intelligence. 2024;128(November 2023):107486.","journal-title":"Engineering Applications of Artificial Intelligence."},{"key":"1137_CR3","first-page":"1","volume":"2024","author":"J Chen","year":"2023","unstructured":"Chen J, Guo Z, Xu X, Jeon G, Camacho D. Artificial intelligence for heart sound classification: A review. Expert Systems. 2024;(December 2023):1\u201320.","journal-title":"Expert Systems."},{"key":"1137_CR4","doi-asserted-by":"crossref","unstructured":"Camara J, Neto A, Pires IM, Villasana MV, Zdravevski E, Cunha A. Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification. Journal of Imaging. 2022;8(2).","DOI":"10.3390\/jimaging8020019"},{"issue":"10","key":"1137_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/ijerph19105971","volume":"19","author":"F D\u2019antoni","year":"2022","unstructured":"D\u2019antoni F, Russo F, Ambrosio L, Bacco L, Vollero L, Vadal\u00e0 G, et al. Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review. International Journal of Environmental Research and Public Health. 2022;19(10):1\u201320.","journal-title":"International Journal of Environmental Research and Public Health."},{"key":"1137_CR6","doi-asserted-by":"crossref","unstructured":"Sokolov A, Paull EO, Stuart JM. One-class detection of cell states in tumor subtypes. Pacific Symposium on Biocomputing. 2016;405\u201316.","DOI":"10.1142\/9789814749411_0037"},{"key":"1137_CR7","doi-asserted-by":"crossref","unstructured":"Stoia S, Lenghel M, Dinu C, Tama\u0219 T, Bran S, B\u0103ciu\u021b M, et al. The Value of Multiparametric Magnetic Resonance Imaging in the Preoperative Differential Diagnosis of Parotid Gland Tumors. Cancers. 2023;15(4).","DOI":"10.3390\/cancers15041325"},{"issue":"8192999","key":"1137_CR8","first-page":"1","volume":"2022","author":"Y Wang","year":"2022","unstructured":"Wang Y, Xie W, Huang S, Feng M, Ke X, Zhong Z, et al. The Diagnostic Value of Ultrasound-Based Deep Learning in Differentiating Parotid Gland Tumors. Journal of Oncology. 2022;2022(8192999):1\u20137.","journal-title":"Journal of Oncology."},{"issue":"1","key":"1137_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/nbm.4408","volume":"34","author":"YJ Chang","year":"2021","unstructured":"Chang YJ, Huang TY, Liu YJ, Chung HW, Juan CJ. Classification of parotid gland tumors by using multimodal MRI and deep learning. NMR in Biomedicine. 2021;34(1):1\u20139.","journal-title":"NMR in Biomedicine."},{"key":"1137_CR10","doi-asserted-by":"crossref","unstructured":"Muntean DD, Dudea SM, B\u0103ciu\u021b M, Dinu C, Stoia S, Solomon C, et al. The Role of an MRI-Based Radiomic Signature in Predicting Malignancy of Parotid Gland Tumors. Cancers. 2023;15(13).","DOI":"10.3390\/cancers15133319"},{"issue":"March","key":"1137_CR11","first-page":"1","volume":"11","author":"Y Xu","year":"2021","unstructured":"Xu Y, Shu Z, Song G, Liu Y, Pang P, Wen X, et al. The Role of Preoperative Computed Tomography Radiomics in Distinguishing Benign and Malignant Tumors of the Parotid Gland. Frontiers in Oncology. 2021;11(March):1\u201312.","journal-title":"Frontiers in Oncology."},{"issue":"2","key":"1137_CR12","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1002\/lary.30154","volume":"133","author":"X Liu","year":"2023","unstructured":"Liu X, Pan Y, Zhang X, Sha Y, Wang S, Li H, et al. A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences. Laryngoscope. 2023;133(2):327\u201335.","journal-title":"Laryngoscope."},{"issue":"1","key":"1137_CR13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"H Matsuo","year":"2020","unstructured":"Matsuo H, Nishio M, Kanda T, Kojita Y, Kono AK, Hori M, et al. Diagnostic accuracy of deep-learning with anomaly detection for a small amount of imbalanced data: discriminating malignant parotid tumors in MRI. Scientific Reports. 2020;10(1):1\u20139.","journal-title":"Scientific Reports."},{"issue":"June","key":"1137_CR14","first-page":"1","volume":"11","author":"X Xia","year":"2021","unstructured":"Xia X, Feng B, Wang J, Hua Q, Yang Y, Sheng L, et al. Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images. Frontiers in Oncology. 2021;11(June):1\u201310.","journal-title":"Frontiers in Oncology."},{"issue":"3","key":"1137_CR15","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1364\/BOE.381257","volume":"11","author":"M Halicek","year":"2020","unstructured":"Halicek M, Dormer JD, Little J V., Chen AY, Fei B. Tumor detection of the thyroid and salivary glands using hyperspectral imaging and deep learning. Biomedical Optics Express. 2020;11(3):1383.","journal-title":"Biomedical Optics Express."},{"key":"1137_CR16","doi-asserted-by":"crossref","unstructured":"Feng B, Xia X, Xu L, Hu C, Wang J, Zhang Z, et al. Deep-Learning for Diagnosis of Parotid Gland Tumor on MR Images. International Journal of Radiation Oncology*Biology*Physics. 2020;108(3):S43\u20134.","DOI":"10.1016\/j.ijrobp.2020.07.2155"},{"key":"1137_CR17","doi-asserted-by":"crossref","unstructured":"Prezioso E, Izzo S, Giampaolo F, Piccialli F, Dell\u2019aversana Orabona G, Cuocolo R, et al. Predictive Medicine for Salivary Gland Tumours Identification Through Deep Learning. IEEE Journal of Biomedical and Health Informatics. 2022;26(10):4869\u201379.","DOI":"10.1109\/JBHI.2021.3120178"},{"issue":"8","key":"1137_CR18","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1111\/odi.14474","volume":"29","author":"XM Shen","year":"2023","unstructured":"Shen XM, Mao L, Yang ZY, Chai ZK, Sun TG, Xu Y, et al. Deep learning-assisted diagnosis of parotid gland tumors by using contrast-enhanced CT imaging. Oral Diseases. 2023;29(8):3325\u201336.","journal-title":"Oral Diseases."},{"issue":"3","key":"1137_CR19","first-page":"1","volume":"35","author":"CJ Juan","year":"2022","unstructured":"Juan CJ, Huang TY, Liu YJ, Shen WC, Wang CW, Hsu K, et al. Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging. NMR in Biomedicine. 2022;35(3):1\u20137.","journal-title":"NMR in Biomedicine."},{"issue":"12","key":"1137_CR20","doi-asserted-by":"crossref","first-page":"8099","DOI":"10.1007\/s00330-022-08943-9","volume":"32","author":"Z He","year":"2022","unstructured":"He Z, Mao Y, Lu S, Tan L, Xiao J, Tan P, et al. Machine learning\u2013based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study. European Radiology. 2022;32(12):8099\u2013110.","journal-title":"European Radiology."},{"key":"1137_CR21","doi-asserted-by":"crossref","unstructured":"Yuan J, Fan Y, Lv X, Chen C, Li D, Hong Y, et al. Research on the Practical Classification and Privacy Protection of CT Images of Parotid Tumors based on ResNet50 Model. Journal of Physics: Conference Series. 2020;1576(1).","DOI":"10.1088\/1742-6596\/1576\/1\/012040"},{"issue":"10","key":"1137_CR22","doi-asserted-by":"crossref","first-page":"6953","DOI":"10.1007\/s00330-022-08830-3","volume":"32","author":"Y Zheng","year":"2022","unstructured":"Zheng Y, Zhou D, Liu H, Wen M. CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors. European Radiology. 2022;32(10):6953\u201364.","journal-title":"European Radiology."},{"issue":"11","key":"1137_CR23","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.1007\/s11517-023-02898-9","volume":"61","author":"N HaLiMaiMaiTi","year":"2023","unstructured":"HaLiMaiMaiTi N, Hong Y, Li M, Li H, Wang Y, Chen C, et al. Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model. Medical and Biological Engineering and Computing. 2023;61(11):3123\u201335.","journal-title":"Medical and Biological Engineering and Computing."},{"key":"1137_CR24","doi-asserted-by":"crossref","unstructured":"Slama A Ben, Mbarki Z, Seddik H, Marrakchi J, Boukriba S, Labidi S. Improving Parotid Gland Tumor Segmentation and Classification Using Geometric Active Contour Model and Deep Neural Network Framework. Traitement du Signal. 2021;38(4):955\u201365.","DOI":"10.18280\/ts.380405"},{"issue":"August","key":"1137_CR25","first-page":"1","volume":"12","author":"Z Hu","year":"2022","unstructured":"Hu Z, Wang B, Pan X, Cao D, Gao A, Yang X, et al. Using deep learning to distinguish malignant from benign parotid tumors on\u00a0plain computed tomography images. Frontiers in Oncology. 2022;12(August):1\u201310.","journal-title":"Frontiers in Oncology."},{"issue":"9","key":"1137_CR26","doi-asserted-by":"crossref","first-page":"6054","DOI":"10.1007\/s00330-023-09568-2","volume":"33","author":"Q Yu","year":"2023","unstructured":"Yu Q, Ning Y, Wang A, Li S, Gu J, Li Q, et al. Deep learning\u2013assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study. European Radiology. 2023;33(9):6054\u201365.","journal-title":"European Radiology."},{"issue":"11","key":"1137_CR27","doi-asserted-by":"crossref","first-page":"5389","DOI":"10.1007\/s00405-022-07455-y","volume":"279","author":"E Gunduz","year":"2022","unstructured":"Gunduz E, Al\u00e7in OF, Kizilay A, Yildirim IO. Deep learning model developed by multiparametric MRI in differential diagnosis of parotid gland tumors. European Archives of Oto-Rhino-Laryngology. 2022;279(11):5389\u201399.","journal-title":"European Archives of Oto-Rhino-Laryngology."},{"key":"1137_CR28","doi-asserted-by":"crossref","unstructured":"Qi J, Gao A, Ma X, Song Y, zhao G, Bai J, et al. Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram. Frontiers in Oncology. 2022;12(July):1\u201312.","DOI":"10.3389\/fonc.2022.937050"},{"key":"1137_CR29","doi-asserted-by":"crossref","unstructured":"Tu CH, Wang RT, Wang B Sen, Kuo CE, Wang EY, Tu CT, et al. Neural network combining with clinical ultrasonography: A new approach for classification of salivary gland tumors. Head and Neck. 2023;45(8):1885\u201393.","DOI":"10.1002\/hed.27396"},{"key":"1137_CR30","doi-asserted-by":"crossref","unstructured":"Zheng Y mei, Li J, Liu S, Cui J fa, Zhan J feng, Pang J, et al. MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland. European Radiology. 2021;31(6):4042\u201352.","DOI":"10.1007\/s00330-020-07483-4"},{"issue":"5","key":"1137_CR31","doi-asserted-by":"crossref","first-page":"2989","DOI":"10.21037\/qims-22-950","volume":"13","author":"G Zhang","year":"2023","unstructured":"Zhang G, Zhu L, Huang R, Xu Y, Lu X, Chen Y, et al. A deep learning model for the differential diagnosis of benign and malignant salivary gland tumors based on ultrasound imaging and clinical data. Quantitative Imaging in Medicine and Surgery. 2023;13(5):2989\u20133000.","journal-title":"Quantitative Imaging in Medicine and Surgery."},{"issue":"2","key":"1137_CR32","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1097\/MOO.0000000000000782","volume":"30","author":"E G\u00fcnd\u00fcz","year":"2022","unstructured":"G\u00fcnd\u00fcz E, Al\u00e7in \u00d6F, Kizilay A, Piazza C. Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors. Current Opinion in Otolaryngology and Head and Neck Surgery. 2022;30(2):107\u201313.","journal-title":"Current Opinion in Otolaryngology and Head and Neck Surgery."},{"key":"1137_CR33","doi-asserted-by":"crossref","unstructured":"Zhang H, Lai H, Wang Y, Lv X, Hong Y, Peng J, et al. Research on the Classification of Benign and Malignant Parotid Tumors Based on Transfer Learning and a Convolutional Neural Network. IEEE Access. 2021;9(Ild):40360\u201371.","DOI":"10.1109\/ACCESS.2021.3064752"},{"issue":"1","key":"1137_CR34","first-page":"1","volume":"12","author":"M Nanavati","year":"2024","unstructured":"Nanavati M, Shah M. Performance comparison of different Wavelet based image fusion techniques for Lumbar Spine Images. Journal of Integrated Science and Technology. 2024;12(1):1\u20139.","journal-title":"Journal of Integrated Science and Technology."},{"issue":"3","key":"1137_CR35","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1006\/gmip.1995.1022","volume":"57","author":"H Li","year":"1995","unstructured":"Li H, Manjunath BS, Mitra SK. Multisensor Image Fusion Using the Wavelet Transform. Graphical Models and Image Processing. 1995;57(3):235\u201345.","journal-title":"Graphical Models and Image Processing."},{"key":"1137_CR36","doi-asserted-by":"crossref","unstructured":"Pal HS, Kumar A, Vishwakarma A, Singh GK, Lee HN. A new automated compression technique for 2D electrocardiogram signals using discrete wavelet transform. Engineering Applications of Artificial Intelligence. 2024;133(PB):108123.","DOI":"10.1016\/j.engappai.2024.108123"},{"key":"1137_CR37","volume-title":"Lectures on Wavelets","author":"Daubechies I Ten","year":"1993","unstructured":"Daubechies I. Ten Lectures on Wavelets. SIAM, Philadelphia. 1993."},{"key":"1137_CR38","doi-asserted-by":"crossref","unstructured":"Gupt AK, Mandal UK, Prasad A. Lebedev \u2013 Skalskaya Transform Related Continuous Wavelet Transform. Results in Mathematics. 2024;0123456789.","DOI":"10.1007\/s00025-024-02130-6"},{"key":"1137_CR39","first-page":"2004","volume":"3","author":"M Misiti","year":"2004","unstructured":"Misiti M, Misiti Y, Oppenheim G, Poggi JM. Matlab Wavelet Toolbox User\u2019s Guide. Version 3. (July 2004). 2004;","journal-title":"Matlab Wavelet Toolbox User\u2019s Guide. Version"},{"key":"1137_CR40","volume-title":"Wavelets for Image Fusion","author":"SG Huang","year":"2010","unstructured":"Huang SG. Wavelets for Image Fusion. Graduate Institute of Communication Engineering and Department of Electrical Engineering, National Taiwan University; 2010."},{"key":"1137_CR41","unstructured":"Matlab wfilters @ www.mathworks.com. Available: https:\/\/www.mathworks.com\/help\/wavelet\/ref\/wfilters.html#d126e142643"},{"issue":"7","key":"1137_CR42","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","volume":"11","author":"SG Mallat","year":"1989","unstructured":"Mallat SG. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1989;11(7):674\u201393.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence."},{"issue":"4","key":"1137_CR43","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.isprsjprs.2007.05.009","volume":"62","author":"K Amolins","year":"2007","unstructured":"Amolins K, Zhang Y, Dare P. Wavelet based image fusion techniques - An introduction, review and comparison. ISPRS Journal of Photogrammetry and Remote Sensing. 2007;62(4):249\u201363.","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing."},{"key":"1137_CR44","volume-title":"An Introduction to Wavelets","author":"CK Chui","year":"1992","unstructured":"Chui CK. An Introduction to Wavelets. Academic Press, New York. 1992."},{"issue":"9","key":"1137_CR45","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1016\/j.patcog.2004.03.010","volume":"37","author":"G Pajares","year":"2004","unstructured":"Pajares G, de la Cruz JM. A wavelet-based image fusion tutorial. Pattern Recognition. 2004;37(9):1855\u201372.","journal-title":"Pattern Recognition."},{"key":"1137_CR46","unstructured":"Matlab dwt2 @ www.mathworks.com. Available: https:\/\/www.mathworks.com\/help\/wavelet\/ref\/dwt2.html"},{"key":"1137_CR47","unstructured":"Matlab wfusimg @ www.mathworks.com. Available: https:\/\/www.mathworks.com\/help\/wavelet\/ref\/wfusimg.html"},{"key":"1137_CR48","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016;2016-Decem:770\u20138.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1137_CR49","unstructured":"Matlab resnet18 @ www.mathworks.com. Available: https:\/\/www.mathworks.com\/help\/deeplearning\/ref\/resnet18.html"},{"key":"1137_CR50","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015;1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1137_CR51","unstructured":"Matlab googlenet @ Www.mathworks.com. Available: https:\/\/www.mathworks.com\/help\/deeplearning\/ref\/googlenet.html"},{"key":"1137_CR52","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten L Van Der, Weinberger KQ. Densely Connected Convolutional Networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017; 2261\u20139.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1137_CR53","unstructured":"Matlab densenet201 @ www.mathworks.com. Available: https:\/\/www.mathworks.com\/help\/deeplearning\/ref\/densenet201.html"},{"key":"1137_CR54","doi-asserted-by":"crossref","unstructured":"Seliya N, Khoshgoftaar TM, Van Hulse J. A study on the relationships of classifier performance metrics. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI. 2009;59\u201366.","DOI":"10.1109\/ICTAI.2009.25"},{"key":"1137_CR55","doi-asserted-by":"crossref","unstructured":"Faghani S, Khosravi B, Zhang K, Moassefi M, Jagtap JM, Nugen F, et al. Mitigating Bias in Radiology Machine Learning: 3. Performance Metrics. Radiology: Artificial Intelligence. 2022;4(5).","DOI":"10.1148\/ryai.220061"},{"issue":"1","key":"1137_CR56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-022-09954-8","volume":"12","author":"SA Hicks","year":"2022","unstructured":"Hicks SA, Str\u00fcmke I, Thambawita V, Hammou M, Riegler MA, Halvorsen P, et al. On evaluation metrics for medical applications of artificial intelligence. Scientific Reports. 2022;12(1):1\u20139.","journal-title":"Scientific Reports."},{"key":"1137_CR57","volume":"146","author":"Z Xu","year":"2022","unstructured":"Xu Z, Chen M, Zheng S, Chen S, Xiao J, Hu Z, et al. Differential diagnosis of parotid gland tumours: Application of SWI combined with DWI and DCE-MRI. European Journal of Radiology. 2022;146:110094.","journal-title":"European Journal of Radiology."},{"issue":"1","key":"1137_CR58","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1186\/s12880-021-00724-y","volume":"21","author":"S Xiang","year":"2021","unstructured":"Xiang S, Ren J, Xia Z, Yuan Y, Tao X. Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging in the\u00a0differential diagnosis of parotid tumors. BMC medical imaging. 2021 Dec;21(1):194.","journal-title":"BMC medical imaging."},{"issue":"7","key":"1137_CR59","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.3174\/ajnr.A2520","volume":"32","author":"A Christe","year":"2011","unstructured":"Christe A, Waldherr C, Hallett R, Zbaeren P, Thoeny H. MR imaging of parotid tumors: typical lesion characteristics in MR imaging\u00a0improve discrimination between benign and malignant disease. AJNR American journal of neuroradiology. 2011 Aug;32(7):1202\u20137.","journal-title":"AJNR American journal of neuroradiology."},{"issue":"216","key":"1137_CR60","volume":"2023","author":"KM Sunnetci","year":"2022","unstructured":"Sunnetci KM, Alkan A. Biphasic majority voting-based comparative COVID-19 diagnosis using chest X-ray images. Expert Systems with Applications. 2023;216(May 2022):119430.","journal-title":"Expert Systems with Applications."},{"key":"1137_CR61","volume":"77","author":"KM Sunnetci","year":"2022","unstructured":"Sunnetci KM, Ulukaya S, Alkan A. Periodontal bone loss detection based on hybrid deep learning and machine learning models with a user-friendly application. Biomedical Signal Processing and Control. 2022;77:103844.","journal-title":"Biomedical Signal Processing and Control."},{"issue":"7","key":"1137_CR62","doi-asserted-by":"crossref","first-page":"3783","DOI":"10.1007\/s11760-023-02606-y","volume":"17","author":"FE O\u011fuz","year":"2023","unstructured":"O\u011fuz FE, Alkan A, Sch\u00f6ler T. Emotion detection from ECG signals with different learning algorithms and automated feature engineering. Signal, Image and Video Processing. 2023;17(7):3783\u201391.","journal-title":"Signal, Image and Video Processing."},{"issue":"01","key":"1137_CR63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JMI.6.1.011005","volume":"6","author":"A H\u00e4nsch","year":"2018","unstructured":"H\u00e4nsch A, Schwier M, Gass T, Morgas T. Evaluation of deep learning methods for parotid gland segmentation from CT images. Journal of Medical Imaging. 2018;6(01):1.","journal-title":"Journal of Medical Imaging."},{"issue":"4","key":"1137_CR64","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/diagnostics13040581","volume":"13","author":"M \u00d6nder","year":"2023","unstructured":"\u00d6nder M, Evli C, T\u00fcrk E, Kazan O, Bayrakdar \u0130\u015e, \u00c7elik \u00d6, et al. Deep-Learning-Based Automatic Segmentation of Parotid Gland on Computed Tomography Images. Diagnostics. 2023;13(4):1\u201310.","journal-title":"Diagnostics."},{"issue":"12","key":"1137_CR65","doi-asserted-by":"crossref","first-page":"7757","DOI":"10.1002\/mp.15290","volume":"48","author":"JC Korte","year":"2021","unstructured":"Korte JC, Hardcastle N, Ng SP, Clark B, Kron T, Jackson P. Cascaded deep learning-based auto-segmentation for head and neck cancer patients: Organs at risk on T2-weighted magnetic resonance imaging. Medical Physics. 2021;48(12):7757\u201372.","journal-title":"Medical Physics."},{"issue":"1","key":"1137_CR66","doi-asserted-by":"crossref","DOI":"10.1002\/cpe.7405","volume":"35","author":"KM Sunnetci","year":"2023","unstructured":"Sunnetci KM, Kaba E, Celiker FB, Alkan A. Comparative parotid gland segmentation by using ResNet-18 and MobileNetV2 based DeepLab v3+ architectures from magnetic resonance images. Concurrency and Computation: Practice and Experience. 2023;35(1):e7405.","journal-title":"Concurrency and Computation: Practice and Experience."},{"issue":"1","key":"1137_CR67","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.acra.2023.04.028","volume":"31","author":"KM Sunnetci","year":"2024","unstructured":"Sunnetci KM, Kaba E, Celiker FB, Alkan A. Deep Network-Based Comprehensive Parotid Gland Tumor Detection. Academic Radiology. 2024;31(1):157\u201367.","journal-title":"Academic Radiology."},{"key":"1137_CR68","volume-title":"Use Of Artificial Intelligence In The Differential Diagnosis Of Parotid Gland Tumors","author":"E Kaba","year":"2023","unstructured":"Kaba E, Celiker FB. Use Of Artificial Intelligence In The Differential Diagnosis Of Parotid Gland Tumors. Faculty of Medicine, Department of Radiology, Recep Tayyip Erdo\u011fan University; 2023."}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01137-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01137-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01137-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,20]],"date-time":"2025-05-20T17:27:03Z","timestamp":1747762023000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01137-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,26]]},"references-count":68,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["1137"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01137-3","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,26]]},"assertion":[{"value":"1 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of University Recep Tayyip Erdogan (2022\/149).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}