{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:03:06Z","timestamp":1772906586593,"version":"3.50.1"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031973123","type":"print"},{"value":"9783031973130","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-97313-0_11","type":"book-chapter","created":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T04:52:36Z","timestamp":1750740756000},"page":"133-146","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Thyroid Nodule Classification Using Convolutional Neural Networks in\u00a0Ultrasound Imaging"],"prefix":"10.1007","author":[{"given":"Athanasios","family":"Kanavos","sequence":"first","affiliation":[]},{"given":"Ioannis","family":"Karamitsos","sequence":"additional","affiliation":[]},{"given":"Manolis","family":"Maragoudakis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,23]]},"reference":[{"key":"11_CR1","unstructured":"DDTI: Thyroid ultrasound images. https:\/\/www.kaggle.com\/datasets\/dasmehdixtr\/ddti-thyroid-ultrasound-images. Accessed 20 Feb 2025"},{"key":"11_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103871","volume":"122","author":"F Abdolali","year":"2020","unstructured":"Abdolali, F., Kapur, J., Jaremko, J.L., Noga, M., Hareendranathan, A.R., Punithakumar, K.: Automated thyroid nodule detection from ultrasound imaging using deep convolutional neural networks. Comput. Biol. Med. 122, 103871 (2020)","journal-title":"Comput. Biol. Med."},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Aditi, Prasad, V.K., Gerogiannis, V.C., Kanavos, A., Dansana, D., Acharya, B.: Utilizing convolutional neural networks for resource allocation bottleneck analysis in cloud ecosystems. Cluster Comput. 28(1), 22 (2025)","DOI":"10.1007\/s10586-024-04720-z"},{"issue":"1","key":"11_CR4","first-page":"687","volume":"43","author":"OA Ajilisa","year":"2022","unstructured":"Ajilisa, O.A., Raj, V., Sabu, M.K.: Segmentation of thyroid nodules from ultrasound images using convolutional neural network architectures. J. Intell. Fuzzy Syst. 43(1), 687\u2013705 (2022)","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"11","key":"11_CR5","doi-asserted-by":"publisher","first-page":"702","DOI":"10.3390\/info15110702","volume":"15","author":"S Akuthota","year":"2024","unstructured":"Akuthota, S., et al.: Enhancing real-time cursor control with motor imagery and deep neural networks for brain-computer interfaces. Information 15(11), 702 (2024)","journal-title":"Information"},{"key":"11_CR6","unstructured":"Bjorck, J., Gomes, C.P., Selman, B., Weinberger, K.Q.: Understanding batch normalization. In: Annual Conference on Neural Information Processing Systems pp. 7705\u20137716 (2018)"},{"issue":"4","key":"11_CR7","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1007\/s10278-017-9997-y","volume":"30","author":"J Chi","year":"2017","unstructured":"Chi, J., Walia, E., Babyn, P.S., Wang, J., Groot, G., Eramian, M.G.: Thyroid nodule classification in ultrasound images by fine-tuning deep convolutional neural network. J. Digit. Imag. 30(4), 477\u2013486 (2017)","journal-title":"J. Digit. Imag."},{"issue":"3","key":"11_CR8","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/s10462-023-10635-9","volume":"57","author":"D Das","year":"2024","unstructured":"Das, D., Iyengar, M.S., Majdi, M.S., Rodriguez, J.J., Alsayed, M.: Deep learning for thyroid nodule examination: a technical review. Artif. Intell. Rev. 57(3), 47 (2024)","journal-title":"Artif. Intell. Rev."},{"issue":"9","key":"11_CR9","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1001\/jama.2018.0898","volume":"319","author":"C Durante","year":"2018","unstructured":"Durante, C., Grani, G., Lamartina, L., Filetti, S., Mandel, S.J., Cooper, D.S.: The diagnosis and management of thyroid nodules: a review. JAMA 319(9), 914\u2013924 (2018)","journal-title":"JAMA"},{"key":"11_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2024.102439","volume":"117","author":"S Gao","year":"2024","unstructured":"Gao, S., Li, Y., Luo, H.: Detecting thyroid nodules along with surrounding tissues and tracking nodules using motion prior in ultrasound videos. Comput. Med. Imaging Graph. 117, 102439 (2024)","journal-title":"Comput. Med. Imaging Graph."},{"key":"11_CR11","volume-title":"Deep Learning","author":"IJ Goodfellow","year":"2016","unstructured":"Goodfellow, I.J., Bengio, Y., Courville, A.C.: Deep Learning. MIT Press, Adaptive Computation and Machine Learning (2016)"},{"issue":"3","key":"11_CR12","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1002\/cncr.30360","volume":"123","author":"BR Haugen","year":"2017","unstructured":"Haugen, B.R.: 2015 American thyroid association management guidelines for adult patients with thyroid nodules and differentiated thyroid cancer: What is new and what has changed? Cancer 123(3), 372\u2013381 (2017)","journal-title":"Cancer"},{"key":"11_CR13","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: 32nd International Conference on Machine Learning (ICML) vol.\u00a037, pp. 448\u2013456 (2015)"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Kanavos, A., Kolovos, E., Papadimitriou, O., Maragoudakis, M.: Breast cancer classification of histopathological images using deep convolutional neural networks. In: 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) pp. 1\u20136. IEEE (2022)","DOI":"10.1109\/SEEDA-CECNSM57760.2022.9932898"},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Kanavos, A., Papadimitriou, O., Al-Hussaeni, K., Karamitsos, I., Maragoudakis, M.: Analyzing deep learning techniques in natural scene image classification. In: IEEE International Conference on Big Data, pp. 5682\u20135691 (2024)","DOI":"10.1109\/BigData62323.2024.10824948"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Kanavos, A., Papadimitriou, O., Kaponis, A., Maragoudakis, M.: Enhancing disease diagnosis: a CNN-based approach for automated white blood cell classification. In: IEEE International Conference on Big Data, pp. 4606\u20134613 (2023)","DOI":"10.1109\/BigData59044.2023.10386168"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Kanavos, A., Papadimitriou, O., Mylonas, P., Maragoudakis, M.: Enhancing sign language recognition using deep convolutional neural networks. In: 14th International Conference on Information, Intelligence, Systems & Applications (IISA) pp. 1\u20134. IEEE (2023)","DOI":"10.1109\/IISA59645.2023.10345865"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Kanavos, A., Papadimitriou, O., Vonitsanos, G., Maragoudakis, M., Mylonas, P.: Advanced CNN architectures for improved garbage image classification: an in-depth evaluation. In: 9th IEEE South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) pp. 85\u201390 (2024)","DOI":"10.1109\/SEEDA-CECNSM63478.2024.00024"},{"key":"11_CR19","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations (ICLR) (2015)"},{"issue":"4","key":"11_CR20","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1002\/hed.25415","volume":"41","author":"SY Ko","year":"2019","unstructured":"Ko, S.Y., et al.: Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound. Head Neck 41(4), 885\u2013891 (2019)","journal-title":"Head Neck"},{"key":"11_CR21","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: 26th Annual Conference on Neural Information Processing Systems, pp. 1106\u20131114 (2012)"},{"issue":"9","key":"11_CR22","doi-asserted-by":"publisher","first-page":"228","DOI":"10.3390\/jimaging10090228","volume":"10","author":"A Kumawat","year":"2024","unstructured":"Kumawat, A., Panda, S., Gerogiannis, V.C., Kanavos, A., Acharya, B., Manika, S.: A hybrid approach for image acquisition methods based on feature-based image registration. J. Imag. 10(9), 228 (2024)","journal-title":"J. Imag."},{"issue":"7553","key":"11_CR23","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Li, W., Cheng, S., Qian, K., Yue, K., Liu, H.: Automatic recognition and classification system of thyroid nodules in CT images based on CNN. Comput. Intell. Neurosci. 2021, 5540186:1\u20135540186:11 (2021)","DOI":"10.1155\/2021\/5540186"},{"key":"11_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101555","volume":"58","author":"T Liu","year":"2019","unstructured":"Liu, T., et al.: Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks. Med. Image Anal. 58, 101555 (2019)","journal-title":"Med. Image Anal."},{"issue":"7","key":"11_CR26","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3390\/jimaging4070095","volume":"4","author":"IE Livieris","year":"2018","unstructured":"Livieris, I.E., Kanavos, A., Tampakas, V., Pintelas, P.E.: An ensemble SSL algorithm for efficient chest x-ray image classification. J. Imag. 4(7), 95 (2018)","journal-title":"J. Imag."},{"issue":"1","key":"11_CR27","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1002\/ima.22363","volume":"30","author":"O Moussa","year":"2020","unstructured":"Moussa, O., Khachnaoui, H., Guetari, R., Khlifa, N.: Thyroid nodules classification and diagnosis in ultrasound images using fine-tuning deep convolutional neural network. Int. J. Imaging Syst. Technol. 30(1), 185\u2013195 (2020)","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Papadimitriou, O., Kanavos, A., Maragoudakis, M.: Automated pneumonia detection from chest x-ray images using deep convolutional neural networks. In: 14th International Conference on Information, Intelligence, Systems & Applications (IISA) pp. 1\u20134. IEEE (2023)","DOI":"10.1109\/IISA59645.2023.10345859"},{"key":"11_CR29","unstructured":"Papadimitriou, O., Kanavos, A., Maragoudakis, M., Gerogiannis, V.C.: Chess piece recognition using deep convolutional neural networks. In: 4th Symposium on Pattern Recognition and Applications (SPRA) vol. 13162, p. 1316202 (2024)"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Papadimitriou, O., Kanavos, A., Mylonas, P., Maragoudakis, M.: Advancing weather image classification using deep convolutional neural networks. In: 18th IEEE International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) pp. 1\u20136 (2023)","DOI":"10.1109\/SMAP59435.2023.10255190"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Papadimitriou, O., Kanavos, A., Mylonas, P., Maragoudakis, M.: Classification of Alzheimer\u2019s disease subjects from MRI using deep convolutional neural networks. In: 3rd International Conference on Novel & Intelligent Digital Systems (NiDS), vol. 784, pp. 277\u2013286 (2023)","DOI":"10.1007\/978-3-031-44146-2_28"},{"key":"11_CR32","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"11_CR33","doi-asserted-by":"crossref","unstructured":"Raju, G., Nikesh, P., Resmi, K.R., Swain, D., Acharya, B., Gerogiannis, V.C., Kanavos, A.: Advanced machine learning techniques for detecting irregularities in skin lesion borders: Enhancing early skin cancer detection. Int. J. Artif. Intell. Tools 33(8), 2450024:1\u20132450024:30 (2024)","DOI":"10.1142\/S0218213024500246"},{"issue":"10","key":"11_CR34","doi-asserted-by":"publisher","first-page":"157","DOI":"10.3390\/a11100157","volume":"11","author":"A Savvopoulos","year":"2018","unstructured":"Savvopoulos, A., Kanavos, A., Mylonas, P., Sioutas, S.: LSTM accelerator for convolutional object identification. Algorithms 11(10), 157 (2018)","journal-title":"Algorithms"},{"issue":"1","key":"11_CR35","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."}],"container-title":["IFIP Advances in Information and Communication Technology","Artificial Intelligence Applications and Innovations. AIAI 2025 IFIP WG 12.5 International Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-97313-0_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T05:02:17Z","timestamp":1750741337000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-97313-0_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031973123","9783031973130"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-97313-0_11","relation":{},"ISSN":["1868-4238","1868-422X"],"issn-type":[{"value":"1868-4238","type":"print"},{"value":"1868-422X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"23 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"IFIP International Conference on Artificial Intelligence Applications and Innovations","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Limassol","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cyprus","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ifipaiai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}