{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T20:45:19Z","timestamp":1775681119138,"version":"3.50.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Rev Socionetwork Strat"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s12626-025-00181-x","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T16:16:26Z","timestamp":1741882586000},"page":"111-130","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["An Advanced Deep Learning Framework for Skin Cancer Classification"],"prefix":"10.1007","volume":"19","author":[{"given":"Muhammad Amir","family":"khan","sequence":"first","affiliation":[]},{"given":"Muhammad Danish","family":"Ali","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4649-2376","authenticated-orcid":false,"given":"Tehseen","family":"Mazhar","sequence":"additional","affiliation":[]},{"given":"Tariq","family":"Shahzad","sequence":"additional","affiliation":[]},{"given":"Waheed Ur","family":"Rehman","sequence":"additional","affiliation":[]},{"given":"Mohammad","family":"Shahid","sequence":"additional","affiliation":[]},{"given":"Habib","family":"Hamam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,13]]},"reference":[{"issue":"7","key":"181_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/ett.3963","volume":"32","author":"A Khamparia","year":"2021","unstructured":"Khamparia, A., Singh, P. K., Rani, P., Samanta, D., Khanna, A., & Bhushan, B. (2021). An internet of health things-driven deep learning framework for detection and classification of skin cancer using transfer learning. Trans Emerg Telecommun Technol, 32(7), 1\u201311. https:\/\/doi.org\/10.1002\/ett.3963","journal-title":"Trans Emerg Telecommun Technol"},{"key":"181_CR2","doi-asserted-by":"publisher","first-page":"104418","DOI":"10.1016\/j.compbiomed.2021.104418","volume":"135","author":"M Abdar","year":"2021","unstructured":"Abdar, M., et al. (2021). Uncertainty quantification in skin cancer classification using three-way decision-based bayesian deep learning. Computers in Biology and Medicine, 135, 104418. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104418","journal-title":"Computers in Biology and Medicine"},{"issue":"2","key":"181_CR3","doi-asserted-by":"publisher","first-page":"512","DOI":"10.3390\/ai3020029","volume":"3","author":"K Aljohani","year":"2022","unstructured":"Aljohani, K., & Turki, T. (2022). Automatic classification of melanoma skin Cancer with deep convolutional neural networks. Ai, 3(2), 512\u2013525. https:\/\/doi.org\/10.3390\/ai3020029","journal-title":"Ai"},{"key":"181_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app112210593","volume":"11","author":"N Kausar","year":"2021","unstructured":"Kausar, N., et al. (2021). Multiclass skin cancer classification using ensemble of fine-tuned deep learning models. Appl Sci, 11, 1\u201320. https:\/\/doi.org\/10.3390\/app112210593","journal-title":"Appl Sci"},{"issue":"3","key":"181_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/bioengineering9030097","volume":"9","author":"S Bechelli","year":"2022","unstructured":"Bechelli, S., & Delhommelle, J. (2022). Machine learning and deep learning algorithms for skin Cancer classification from dermoscopic images. Bioengineering, 9(3), 1\u201318. https:\/\/doi.org\/10.3390\/bioengineering9030097","journal-title":"Bioengineering"},{"issue":"8","key":"181_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/biom10081123","volume":"10","author":"S Jinnai","year":"2020","unstructured":"Jinnai, S., Yamazaki, N., Hirano, Y., Sugawara, Y., Ohe, Y., & Hamamoto, R. (2020). The development of a skin cancer classification system for pigmented skin lesions using deep learning. Biomolecules, 10(8), 1\u201313. https:\/\/doi.org\/10.3390\/biom10081123","journal-title":"Biomolecules"},{"issue":"11","key":"181_CR7","doi-asserted-by":"publisher","first-page":"101","DOI":"10.46501\/ijmtst061118","volume":"6","author":"S Ravi Manne","year":"2020","unstructured":"Ravi Manne, S., Kantheti, & Kantheti, S. (2020). Classification of skin cancer using deep learning, convolutional neural Networks - Opportunities and vulnerabilities- A systematic review. Int J Mod Trends Sci Technol, 6(11), 101\u2013108. https:\/\/doi.org\/10.46501\/ijmtst061118","journal-title":"Int J Mod Trends Sci Technol"},{"key":"181_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ATSIP49331.2020.9231544","volume":"2020","author":"J Daghrir","year":"2020","unstructured":"Daghrir, J., Tlig, L., Bouchouicha, M., & Sayadi, M. (2020). Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach. 2020 Int Conf Adv Technol Signal Image Process ATSIP, 2020, 1\u20135. https:\/\/doi.org\/10.1109\/ATSIP49331.2020.9231544","journal-title":"2020 Int Conf Adv Technol Signal Image Process ATSIP"},{"issue":"1","key":"181_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/diagnostics12010040","volume":"12","author":"M Nauta","year":"2022","unstructured":"Nauta, M., Walsh, R., Dubowski, A., & Seifert, C. (2022). Uncovering and correcting shortcut learning in machine learning models for skin cancer diagnosis. Diagnostics, 12(1), 1\u201318. https:\/\/doi.org\/10.3390\/diagnostics12010040","journal-title":"Diagnostics"},{"issue":"9","key":"181_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/electronics11091294","volume":"11","author":"I Kousis","year":"2022","unstructured":"Kousis, I., Perikos, I., Hatzilygeroudis, I., & Virvou, M. (2022). Deep learning methods for accurate skin Cancer recognition and mobile application. Electron, 11(9), 1\u201319. https:\/\/doi.org\/10.3390\/electronics11091294","journal-title":"Electron"},{"issue":"7","key":"181_CR11","doi-asserted-by":"publisher","first-page":"1183","DOI":"10.3390\/healthcare10071183","volume":"10","author":"W Gouda","year":"2022","unstructured":"Gouda, W., et al. (2022). Detection of skin Cancer based on skin lesion images using deep learning. Healthcare, 10(7), 1183. https:\/\/doi.org\/10.3390\/healthcare10071183","journal-title":"Healthcare"},{"key":"181_CR12","doi-asserted-by":"publisher","unstructured":"Mazhar, T., et al. (2023). The role of machine learning and deep learning approaches for the detection of skin Cancer. Healthc, 11(3). https:\/\/doi.org\/10.3390\/healthcare11030415","DOI":"10.3390\/healthcare11030415"},{"key":"181_CR13","doi-asserted-by":"publisher","unstructured":"Dildar, M., et al. (2021). Skin cancer detection: A review using deep learning techniques. International Journal of Environmental Research and Public Health, 18(10). https:\/\/doi.org\/10.3390\/ijerph18105479","DOI":"10.3390\/ijerph18105479"},{"key":"181_CR14","doi-asserted-by":"publisher","unstructured":"Das, K., et al. (2021). Machine learning and its application in skin cancer. International Journal of Environmental Research and Public Health, 18(24). https:\/\/doi.org\/10.3390\/ijerph182413409","DOI":"10.3390\/ijerph182413409"},{"key":"181_CR15","doi-asserted-by":"publisher","unstructured":"Kadampur, M. A., & Al Riyaee, S. (2019). Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images, Informatics Med. Unlocked, vol. 18, no. August p. 100282, 2020. https:\/\/doi.org\/10.1016\/j.imu.2019.100282","DOI":"10.1016\/j.imu.2019.100282"},{"key":"181_CR16","doi-asserted-by":"publisher","first-page":"147858","DOI":"10.1109\/ACCESS.2020.3014701","volume":"8","author":"R Ashraf","year":"2020","unstructured":"Ashraf, R., et al. (2020). Region-of-Interest based transfer learning assisted framework for skin Cancer detection. Ieee Access: Practical Innovations, Open Solutions, 8, 147858\u2013147871. https:\/\/doi.org\/10.1109\/ACCESS.2020.3014701","journal-title":"Ieee Access: Practical Innovations, Open Solutions"},{"key":"181_CR17","doi-asserted-by":"publisher","unstructured":"Adegun, A., & Viriri, S. (2021). Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art, vol. 54, no. 2. Springer Netherlands. https:\/\/doi.org\/10.1007\/s10462-020-09865-y","DOI":"10.1007\/s10462-020-09865-y"},{"issue":"17","key":"181_CR18","doi-asserted-by":"publisher","first-page":"26255","DOI":"10.1007\/s11042-021-10952-7","volume":"80","author":"MM Mijwil","year":"2021","unstructured":"Mijwil, M. M. (2021). Skin cancer disease images classification using deep learning solutions. Multimed Tools Appl, 80(17), 26255\u201326271. https:\/\/doi.org\/10.1007\/s11042-021-10952-7","journal-title":"Multimed Tools Appl"},{"key":"181_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-023-14697-3","author":"JV Tembhurne","year":"2023","unstructured":"Tembhurne, J. V., Hebbar, N., Patil, H. Y., & Diwan, T. (2023). Skin cancer detection using ensemble of machine learning and deep learning techniques. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-023-14697-3","journal-title":"Multimed Tools Appl"},{"issue":"3","key":"181_CR20","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1007\/s11517-021-02473-0","volume":"60","author":"<number>20.<\/number>","year":"2022","unstructured":"(2022). InSiNet: A deep convolutional approach to skin cancer detection and segmentation. Medical & Biological Engineering & Computing, 60(3), 643\u2013662. https:\/\/doi.org\/10.1007\/s11517-021-02473-0","journal-title":"Medical & Biological Engineering & Computing"},{"key":"181_CR21","doi-asserted-by":"publisher","unstructured":"Magalhaes, C., Tavares, J. M. R. S., Mendes, J., & Vardasca, R. (April, 2021). Comparison of machine learning strategies for infrared thermography of skin cancer. Biomedical Signal Processing and Control, 69, no.. https:\/\/doi.org\/10.1016\/j.bspc.2021.102872","DOI":"10.1016\/j.bspc.2021.102872"},{"key":"181_CR22","doi-asserted-by":"publisher","unstructured":"Kalaiarasan, R., Madhan Kumar, K., Sridhar, S., & Yuvarai, M. (2022). Deep learning-based transfer learning for classification of skin Cancer. Proc - Int Conf Appl Artif Intell Comput ICAAIC 2022, 450\u2013454. https:\/\/doi.org\/10.1109\/ICAAIC53929.2022.9792651","DOI":"10.1109\/ICAAIC53929.2022.9792651"},{"key":"181_CR23","doi-asserted-by":"publisher","unstructured":"(2022). Skin Cancer detection using Combined Decision of Deep Learners, IEEE Access, vol. 10, no. October, pp. 118198\u2013118212. https:\/\/doi.org\/10.1109\/ACCESS.2022.3220329","DOI":"10.1109\/ACCESS.2022.3220329"},{"key":"181_CR24","doi-asserted-by":"publisher","first-page":"107006","DOI":"10.1016\/j.optlastec.2021.107006","volume":"140","author":"Y Wang","year":"2021","unstructured":"Wang, Y., et al. (2021). Deep learning enhances polarization speckle for in vivo skin cancer detection. Optics & Laser Technology, 140, 107006. https:\/\/doi.org\/10.1016\/j.optlastec.2021.107006","journal-title":"Optics & Laser Technology"},{"issue":"4","key":"181_CR25","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1007\/s00530-021-00787-5","volume":"28","author":"M Shorfuzzaman","year":"2022","unstructured":"Shorfuzzaman, M. (2022). An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection. Multimed Syst, 28(4), 1309\u20131323. https:\/\/doi.org\/10.1007\/s00530-021-00787-5","journal-title":"Multimed Syst"},{"issue":"S1","key":"181_CR26","doi-asserted-by":"publisher","first-page":"S307","DOI":"10.3233\/THC-174633","volume":"26","author":"X He","year":"2018","unstructured":"He, X., Yu, Z., Wang, T., Lei, B., & Shi, Y. (2018). Dense Deconvolution net: Multi path fusion and dense Deconvolution for high resolution skin lesion segmentation. Technol Heal Care, 26(S1), S307\u2013S316. https:\/\/doi.org\/10.3233\/THC-174633","journal-title":"Technol Heal Care"},{"key":"181_CR27","doi-asserted-by":"publisher","unstructured":"Moldovan, D. (2019). Transfer learning based method for two-step skin cancer images classification. 2019 7th E-Health Bioeng Conf EHB 2019, 31\u201334. https:\/\/doi.org\/10.1109\/EHB47216.2019.8970067","DOI":"10.1109\/EHB47216.2019.8970067"},{"key":"181_CR28","doi-asserted-by":"publisher","DOI":"10.1109\/ATEE.2019.8724938","author":"SR \u015etefan Jianu","year":"2019","unstructured":"\u015etefan Jianu, S. R., Ichim, L., & Popescu, D. (2019). Automatic diagnosis of skin Cancer using neural networks. 2019 11th Int Symp Adv Top Electr Eng ATEE 2019. https:\/\/doi.org\/10.1109\/ATEE.2019.8724938","journal-title":"2019 11th Int Symp Adv Top Electr Eng ATEE 2019"},{"key":"181_CR29","doi-asserted-by":"publisher","unstructured":"Goyal, M., Yap, M. H., & Hassanpour, S. (2020). Multi-class semantic segmentation of skin lesions via fully convolutional networks, Bioinforma.\u201311th Int. Conf. Bioinforma. Model. Methods Algorithms, Proceedings; Part 13th Int. Jt. Conf. Biomed. Eng. Syst. Technol. BIOSTEC 2020, vol. 3, no. Biostec 2020, pp. 290\u2013294, 2020. https:\/\/doi.org\/10.5220\/0009380302900295","DOI":"10.5220\/0009380302900295"},{"key":"181_CR30","doi-asserted-by":"publisher","unstructured":"Khan, I. U. (2021). Remote Diagnosis and Triaging Model for Skin Cancer Using EfficientNet and Extreme Gradient Boosting, Complexity, vol. 2021. https:\/\/doi.org\/10.1155\/2021\/5591614","DOI":"10.1155\/2021\/5591614"},{"key":"181_CR31","doi-asserted-by":"publisher","first-page":"129668","DOI":"10.1109\/ACCESS.2020.3009276","volume":"8","author":"MA Anjum","year":"2020","unstructured":"Anjum, M. A., Amin, J., Sharif, M., Khan, H. U., Malik, M. S. A., & Kadry, S. (2020). Deep semantic segmentation and Multi-Class skin lesion classification based on convolutional neural network. Ieee Access: Practical Innovations, Open Solutions, 8, 129668\u2013129678. https:\/\/doi.org\/10.1109\/ACCESS.2020.3009276","journal-title":"Ieee Access: Practical Innovations, Open Solutions"},{"key":"181_CR32","doi-asserted-by":"publisher","unstructured":"Bassel, A., Abdulkareem, A. B., Alyasseri, Z. A. A., Sani, N. S., & Mohammed, H. J. (2022). Automatic malignant and benign skin Cancer classification using a hybrid deep learning approach. Diagnostics, 12(10). https:\/\/doi.org\/10.3390\/diagnostics12102472","DOI":"10.3390\/diagnostics12102472"},{"key":"181_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/s11063-022-10927-1","author":"H Tabrizchi","year":"2022","unstructured":"Tabrizchi, H., Parvizpour, S., & Razmara, J. (2022). An improved VGG model for skin Cancer detection. Neural Process Lett No June. https:\/\/doi.org\/10.1007\/s11063-022-10927-1","journal-title":"Neural Process Lett No June"},{"key":"181_CR34","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1007\/s11042-020-09388-2","volume":"79","author":"SS Chaturvedi","year":"2020","unstructured":"Chaturvedi, S. S., Tembhurne, J. V., & Diwan, T. (2020). A multi-class skin Cancer classification using deep convolutional neural networks. Multimed Tools Appl, 79, 39\u201340. https:\/\/doi.org\/10.1007\/s11042-020-09388-2","journal-title":"Multimed Tools Appl"},{"issue":"5","key":"181_CR35","doi-asserted-by":"publisher","first-page":"3073","DOI":"10.1007\/s11063-020-10364-y","volume":"53","author":"K Thurnhofer-Hemsi","year":"2021","unstructured":"Thurnhofer-Hemsi, K., & Dom\u00ednguez, E. (2021). A convolutional neural network framework for accurate skin Cancer detection. Neural Processing Letters, 53(5), 3073\u20133093. https:\/\/doi.org\/10.1007\/s11063-020-10364-y","journal-title":"Neural Processing Letters"},{"issue":"8","key":"181_CR36","doi-asserted-by":"publisher","first-page":"9909","DOI":"10.1007\/s11042-018-5714-1","volume":"77","author":"UO Dorj","year":"2018","unstructured":"Dorj, U. O., Lee, K. K., Choi, J. Y., & Lee, M. (2018). The skin cancer classification using deep convolutional neural network. Multimed Tools Appl, 77(8), 9909\u20139924. https:\/\/doi.org\/10.1007\/s11042-018-5714-1","journal-title":"Multimed Tools Appl"},{"key":"181_CR37","doi-asserted-by":"publisher","unstructured":"Amelard, R., Wong, A., & Clausi, D. A. (2012). Extracting morphological high-level intuitive features (HLIF) for enhancing skin lesion classification. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS, 4458\u20134461. https:\/\/doi.org\/10.1109\/EMBC.2012.6346956","DOI":"10.1109\/EMBC.2012.6346956"},{"key":"181_CR38","doi-asserted-by":"publisher","unstructured":"Liu, X., Wang, X., & Matwin, S. (2018). Interpretable deep convolutional neural networks via Meta-learning. Proc Int Jt Conf Neural Networks, 2018-July. https:\/\/doi.org\/10.1109\/IJCNN.2018.8489172","DOI":"10.1109\/IJCNN.2018.8489172"},{"key":"181_CR39","unstructured":"Lopez, A. R. (2017). Skin lesion classification from dermoscopic images using deep learning techniques. 13th IASTED international conference on biomedical engineering (BioMed). IEEE, 2017."},{"key":"181_CR40","doi-asserted-by":"publisher","unstructured":"Hosseinzadeh Kassani, S., & Hosseinzadeh Kassani, P. (2019). A comparative study of deep learning architectures on melanoma detection, Tissue Cell, vol. 58, no. April, pp. 76\u201383. https:\/\/doi.org\/10.1016\/j.tice.2019.04.009","DOI":"10.1016\/j.tice.2019.04.009"},{"key":"181_CR41","doi-asserted-by":"publisher","first-page":"7160","DOI":"10.1109\/ACCESS.2019.2962812","volume":"8","author":"AA Adegun","year":"2020","unstructured":"Adegun, A. A., & Viriri, S. (2020). Deep learning-based system for automatic melanoma detection. Ieee Access: Practical Innovations, Open Solutions, 8, 7160\u20137172. https:\/\/doi.org\/10.1109\/ACCESS.2019.2962812","journal-title":"Ieee Access: Practical Innovations, Open Solutions"},{"issue":"12","key":"181_CR42","doi-asserted-by":"publisher","first-page":"2130","DOI":"10.1049\/iet-ipr.2018.6669","volume":"13","author":"R Sarkar","year":"2019","unstructured":"Sarkar, R., Chatterjee, C. C., & Hazra, A. (2019). Diagnosis of melanoma from dermoscopic images using a deep depthwise separable residual convolutional network. Iet Image Processing \/ Iet, 13(12), 2130\u20132142. https:\/\/doi.org\/10.1049\/iet-ipr.2018.6669","journal-title":"Iet Image Processing \/ Iet"},{"key":"181_CR43","doi-asserted-by":"publisher","unstructured":"Banerjee, S., Singh, S. K., Chakraborty, A., Das, A., & Bag, R. (2020). Melanoma diagnosis using deep learning and fuzzy logic. Diagnostics, 10(8). https:\/\/doi.org\/10.3390\/diagnostics10080577","DOI":"10.3390\/diagnostics10080577"},{"key":"181_CR44","doi-asserted-by":"publisher","unstructured":"Pham, H. N., et al. (2019). Lesion segmentation and automated melanoma detection using deep convolutional neural networks and XGBoost. Proc 2019 Int Conf Syst Sci Eng ICSSE 2019, 142\u2013147. https:\/\/doi.org\/10.1109\/ICSSE.2019.8823129","DOI":"10.1109\/ICSSE.2019.8823129"},{"issue":"4","key":"181_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/E22040484","volume":"22","author":"JA Almaraz-Damian","year":"2020","unstructured":"Almaraz-Damian, J. A., Ponomaryov, V., Sadovnychiy, S., & Castillejos-Fernandez, H. (2020). Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures. Entropy, 22(4), 1\u201323. https:\/\/doi.org\/10.3390\/E22040484","journal-title":"Entropy"},{"key":"181_CR46","doi-asserted-by":"publisher","unstructured":"Hossin, M. A., Rupom, F. F., Mahi, H. R., Sarker, A., Ahsan, F., & Warech, S. (2020). Melanoma skin cancer detection using deep learning and advanced regularizer. 2020 Int Conf Adv Comput Sci Inf Syst ICACSIS 2020, 89\u201394. https:\/\/doi.org\/10.1109\/ICACSIS51025.2020.9263118","DOI":"10.1109\/ICACSIS51025.2020.9263118"},{"key":"181_CR47","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.cmpb.2018.05.027","volume":"162","author":"MA Al-masni","year":"2018","unstructured":"Al-masni, M. A., Al-antari, M. A., Choi, M. T., Han, S. M., & Kim, T. S. (2018). Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks. Computer Methods and Programs in Biomedicine, 162, 221\u2013231. https:\/\/doi.org\/10.1016\/j.cmpb.2018.05.027","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"181_CR48","doi-asserted-by":"publisher","first-page":"54386","DOI":"10.1109\/ACCESS.2021.3074422","volume":"9","author":"Z Khan","year":"2021","unstructured":"Khan, Z., Khan, F. G., Khan, A., Rehman, Z. U., Shah, S., Qummar, S., Ali, F., & Pack, S. (2021). Diabetic retinopathy detection using VGG-NIN: A deep learning architecture. Ieee Access: Practical Innovations, Open Solutions, 9, 54386\u201354395. https:\/\/doi.org\/10.1109\/ACCESS.2021.3074422","journal-title":"Ieee Access: Practical Innovations, Open Solutions"}],"container-title":["The Review of Socionetwork Strategies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12626-025-00181-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12626-025-00181-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12626-025-00181-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T08:07:30Z","timestamp":1744272450000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12626-025-00181-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,13]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["181"],"URL":"https:\/\/doi.org\/10.1007\/s12626-025-00181-x","relation":{},"ISSN":["2523-3173","1867-3236"],"issn-type":[{"value":"2523-3173","type":"print"},{"value":"1867-3236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,13]]},"assertion":[{"value":"17 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 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":"Not Applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}