{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T16:40:21Z","timestamp":1733071221690,"version":"3.30.0"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031782008"},{"type":"electronic","value":"9783031782015"}],"license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"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\/978-3-031-78201-5_15","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T15:09:12Z","timestamp":1733065752000},"page":"226-241","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Brain Tumor Diagnosis: A Hybrid Approach Using Edge Detection and\u00a0Deep Learning"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0009-0002-4921-9823","authenticated-orcid":false,"given":"Ha Anh","family":"Vu","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0717-9619","authenticated-orcid":false,"given":"Szil\u00e1rd","family":"Vajda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Moallem, G., et al.: Detecting and segmenting overlapping red blood cells in microscopic images of thin blood smears. In: Tomaszewski, J.E., Gurcan, M.N., (eds.) Medical Imaging 2018: Digital Pathology, Houston. SPIE Proceedings, vol. 10581, p. 105811F. Texas, United States, 10-15 February 2018 (2018)","DOI":"10.1117\/12.2293762"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Nkouanga, H.Y., Vajda, S.: Automatic tuberculosis detection using chest x-ray analysis with position enhanced structural information. In: 25th International Conference on Pattern Recognition, ICPR 2020, Virtual Event \/ Milan, Italy, January 10-15, 2021, pp. 6439\u20136446. IEEE (2020)","DOI":"10.1109\/ICPR48806.2021.9412430"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Vajda, S., et al.: Feature selection for automatic tuberculosis screening in frontal chest radiographs. J. Med. Syst. 42(8):146:1\u2013146:11 (2018)","DOI":"10.1007\/s10916-018-0991-9"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Kaifi, R.: A review of recent advances in brain tumor diagnosis based on AI-based classification. Diagnostics, 13(18) (2023)","DOI":"10.3390\/diagnostics13183007"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Abdusalomov, A.B., Mukhiddinov, M., Whangbo, T.K.: Brain tumor detection based on deep learning approaches and magnetic resonance imaging. Cancers, 15(16) (2023)","DOI":"10.3390\/cancers15164172"},{"key":"15_CR6","doi-asserted-by":"publisher","first-page":"e18222","DOI":"10.1016\/j.heliyon.2023.e18222","volume":"9","author":"I Ilic","year":"2023","unstructured":"Ilic, I., Ili\u0107, M.: International patterns and trends in the brain cancer incidence and mortality: an observational study based on the global burden of disease. Heliyon 9, e18222 (2023)","journal-title":"Heliyon"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Soumick, C., Faraz, N., N\u00fcrnberger, A., Oliver, S.: Classification of brain tumours in MR images using deep spatiospatial models. Sci. Rep. 12(1) (2022)","DOI":"10.1038\/s41598-022-05572-6"},{"key":"15_CR8","doi-asserted-by":"publisher","first-page":"106405","DOI":"10.1016\/j.compbiomed.2022.106405","volume":"152","author":"R Ranjbarzadeh","year":"2023","unstructured":"Ranjbarzadeh, R., Caputo, A., Tirkolaee, E.B., Ghoushchi, S.J., Bendechache, M.: Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools. Comput. Biol. Med. 152, 106405 (2023)","journal-title":"Comput. Biol. Med."},{"issue":"3","key":"15_CR9","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.3390\/curroncol30030203","volume":"30","author":"M Ce","year":"2023","unstructured":"Ce, M., et al.: Artificial intelligence in brain tumor imaging: a step toward personalized medicine. Current Oncol. 30(3), 2673\u20132701 (2023)","journal-title":"Current Oncol."},{"key":"15_CR10","doi-asserted-by":"publisher","first-page":"101940","DOI":"10.1016\/j.compmedimag.2021.101940","volume":"91","author":"M Nazir","year":"2021","unstructured":"Nazir, M., Shakil, S., Khurshid, K.: Role of deep learning in brain tumor detection and classification (2015 to 2020): a review. Comput. Med. Imaging Graph. 91, 101940 (2021)","journal-title":"Comput. Med. Imaging Graph."},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Sarfarazi, S., Toygar, \u00d6.: Classification of brain tumors on MRI images using deep learning architectures. In: 9th International IFS Contemporary Mathematics and Engineering Conference Special Issue, pp. 1177\u20131186 (2023)","DOI":"10.17780\/ksujes.1339884"},{"key":"15_CR12","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Liu, S., Deng, W.: Very deep convolutional neural network based image classification using small training sample size. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 730\u2013734 (2015)","DOI":"10.1109\/ACPR.2015.7486599"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Khan, M.S.I., et\u00a0al.: Accurate brain tumor detection using deep convolutional neural network. Comput. Struct. Biotechnol. J. 20(2022) (2020)","DOI":"10.1016\/j.csbj.2022.08.039"},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Israk, F., Soumik, M., Ali, M.: Brain tumor classification with inception network based deep learning model using transfer learning. In: 2020 IEEE Region 10 Symposium (TENSYMP), pp. 1018\u20131021 (2020)","DOI":"10.1109\/TENSYMP50017.2020.9230618"},{"key":"15_CR17","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.compmedimag.2019.05.001","volume":"75","author":"ZN Swati","year":"2019","unstructured":"Swati, Z.N., Zhao, Q., Kabir, M., et al.: Brain tumor classification for MR images using transfer learning and fine-tuning. Comput. Med. Imaging Graph. 75, 34\u201346 (2019)","journal-title":"Comput. Med. Imaging Graph."},{"key":"15_CR18","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1007\/s40998-021-00426-9","volume":"45","author":"E Irmak","year":"2021","unstructured":"Irmak, E.: Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework. Iran. J. Sci. Technol. Trans. Electr. Eng. 45, 1015\u20131036 (2021)","journal-title":"Iran. J. Sci. Technol. Trans. Electr. Eng."},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Rasheed, Z., et al.: Brain tumor classification from MRI using image enhancement and convolutional neural network techniques, vol. 13 (2023)","DOI":"10.3390\/brainsci13091320"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Akshya, K.S., Priyadarsan, P., Muralibabu, K.: Effective use of clustering techniques for brain tumor segmentation. In: 2023 IEEE 3rd International Conference on Applied Electromagnetics, Signal Processing, & Communication (AESPC), pp. 1\u20134 (2023)","DOI":"10.1109\/AESPC59761.2023.10390467"},{"key":"15_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1007\/978-3-030-11723-8_21","volume-title":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","author":"Y Zhou","year":"2019","unstructured":"Zhou, Y., et al.: Holistic brain tumor screening and classification based on DenseNet and recurrent neural network. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11383, pp. 208\u2013217. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-11723-8_21"},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Montoya, S.F.A., Rojas, A.E., V\u00e1squez, L.F.N.: Classification of brain tumors: a comparative approach of shallow and deep neural networks. SN Comput. Sci. 5(142) (2024)","DOI":"10.1007\/s42979-023-02431-7"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Dheepak, G., Christaline, J.A., Vaishali, D.: Mehw-svm multi-kernel approach for improved brain tumour classification. IET Image Process. (2023)","DOI":"10.1049\/ipr2.12990"},{"issue":"10","key":"15_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0140381","volume":"10","author":"J Cheng","year":"2015","unstructured":"Cheng, J., et al.: Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLOS ONE 10(10), 1\u201313 (2015)","journal-title":"PLOS ONE"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Raza, A., et al.: A hybrid deep learning-based approach for brain tumor classification. Electronics, 11(7) (2022)","DOI":"10.3390\/electronics11071146"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Ullah, M.S., Attique Khan, M., Masood, A., Mzoughi, O., Saidani, O., Alturki, N.: Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm. Front. Oncol. 14 (2024)","DOI":"10.3389\/fonc.2024.1335740"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Afshar, P., Plataniotis, K.N., Mohammadi, A.: Capsule networks for brain tumor classification based on mri images and coarse tumor boundaries. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1368\u20131372 (2019)","DOI":"10.1109\/ICASSP.2019.8683759"},{"key":"15_CR28","first-page":"2021","volume":"1\u201313","author":"SR Gunasekara","year":"2021","unstructured":"Gunasekara, S.R., Kaldera, H.N.T.K., Dissanayake, M.B.: A systematic approach for MRI brain tumor localization and segmentation using deep learning and active contouring. J. Healthc. Eng. 1\u201313, 2021 (2021)","journal-title":"J. Healthc. Eng."},{"key":"15_CR29","unstructured":"Cheng, J.: Brain tumor dataset 2017 (2024)"},{"key":"15_CR30","doi-asserted-by":"publisher","first-page":"e02743","DOI":"10.1016\/j.heliyon.2019.e02743","volume":"5","author":"A Bharodiya","year":"2019","unstructured":"Bharodiya, A., Gonsai, A.: An improved edge detection algorithm for x-ray images based on the statistical range. Heliyon 5, e02743 (2019)","journal-title":"Heliyon"},{"issue":"6","key":"15_CR31","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"8","author":"J Canny","year":"1986","unstructured":"Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 8(6), 679\u2013698 (1986)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. PAMI"},{"key":"15_CR32","unstructured":"Gedraite, E.S., Hadad, M.: Investigation on the effect of a gaussian blur in image filtering and segmentation. In: Proceedings ELMAR-2011, pp. 393\u2013396 (2011)"},{"issue":"11","key":"15_CR33","first-page":"68","volume":"50","author":"SK Katiyar","year":"2012","unstructured":"Katiyar, S.K., Arun, P.V.: Comparative analysis of common edge detection techniques in context of object extraction. IEEE Trans. Geosci. Remote Sens. 50(11), 68\u201379 (2012)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"15_CR34","doi-asserted-by":"crossref","unstructured":"Acton, S.T.: Chapter 20 - diffusion partial differential equations for edge detection. In: Bovik, A., (ed.) The Essential Guide to Image Processing, pp. 525\u2013552. Academic Press, Boston (2009)","DOI":"10.1016\/B978-0-12-374457-9.00020-2"},{"key":"15_CR35","doi-asserted-by":"crossref","unstructured":"Vujovic, Z.D.: Classification model evaluation metrics. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 12(6) (2021)","DOI":"10.14569\/IJACSA.2021.0120670"},{"key":"15_CR36","doi-asserted-by":"crossref","unstructured":"Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27(8), 861\u2013874 (2006). ROC Analysis in Pattern Recognition","DOI":"10.1016\/j.patrec.2005.10.010"},{"key":"15_CR37","unstructured":"TensorFlow. tf.keras.preprocessing.image.ImageDataGenerator documentation (2023). Accessed 24 Mar 2024"},{"key":"15_CR38","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)"},{"key":"15_CR39","unstructured":"Abadi, M., Agarwal, A., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems. Software available from tensorflow.org"},{"key":"15_CR40","unstructured":"Chollet, F et\u00a0al.: Keras (2015)"},{"key":"15_CR41","unstructured":"Bradski, G.: The OpenCV library. Dr. Dobb\u2019s J. Softw. Tools (2000)"},{"issue":"7825","key":"15_CR42","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1038\/s41586-020-2649-2","volume":"585","author":"CR Harris","year":"2020","unstructured":"Harris, C.R., et al.: Array programming with NumPy. Nature 585(7825), 357\u2013362 (2020)","journal-title":"Nature"},{"issue":"3","key":"15_CR43","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter, J.D.: Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9(3), 90\u201395 (2007)","journal-title":"Comput. Sci. Eng."},{"issue":"60","key":"15_CR44","doi-asserted-by":"publisher","first-page":"3021","DOI":"10.21105\/joss.03021","volume":"6","author":"ML Waskom","year":"2021","unstructured":"Waskom, M.L.: seaborn: statistical data visualization. J. Open Source Softw. 6(60), 3021 (2021)","journal-title":"J. Open Source Softw."},{"key":"15_CR45","doi-asserted-by":"crossref","unstructured":"Masood, M., et al.: A novel deep learning method for recognition and classification of brain tumors from MRI images. Diagnostics, 11(5) (2021)","DOI":"10.3390\/diagnostics11050744"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78201-5_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T16:03:19Z","timestamp":1733068999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78201-5_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"ISBN":["9783031782008","9783031782015"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78201-5_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"2 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}