Empowering brain cancer diagnosis: harnessing artificial intelligence for advanced imaging insights

References

Abu-Srhan, A., Almallahi, I., Abushariah, M.A.M., Mahafza, W., and Al-Kadi, O.S. (2021). Paired-unpaired Unsupervised Attention Guided GAN with transfer learning for bidirectional brain MR-CT synthesis. Comput. Biol. Med. 136: 104763, https://doi.org/10.1016/j.compbiomed.2021.104763.Search in Google Scholar PubMed

Ahmed, F., Fattani, M.T., Ali, S.R., and Enam, R.N. (2022). Strengthening the bridge between academic and the industry through the academia-industry collaboration plan design model. Front. Psychol. 13: 875940, https://doi.org/10.3389/fpsyg.2022.875940.Search in Google Scholar PubMed PubMed Central

Albawi, S., Mohammed, T.A., and Al-Zawi, S. (2017). Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology. IEEE, Antalya, Turkey, pp. 1–6.10.1109/ICEngTechnol.2017.8308186Search in Google Scholar

Al-Emaryeen, R., Al-Nahhas, S., Himour, F., Mahafza, W., and Al-Kadi, O. (2023). Deepfake image generation for improved brain tumor segmentation. In: 2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, pp. 6–11.10.1109/JEEIT58638.2023.10185710Search in Google Scholar

Ali, M., Gilani, S.O., Waris, A., Zafar, K., and Jamil, M. (2020). Brain tumour image segmentation using deep networks. IEEE Access 8: 153589–153598, https://doi.org/10.1109/access.2020.3018160.Search in Google Scholar

Al-Kadi, O.S. (2009). Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques, PhD Thesis. Brighton, University of Sussex.Search in Google Scholar

Al-Kadi, O.S. (2010). Assessment of texture measures susceptibility to noise in conventional and contrast enhanced computed tomography lung tumour images. Comput. Med. Imaging Graph. 34: 494–503, https://doi.org/10.1016/j.compmedimag.2009.12.011.Search in Google Scholar PubMed

Al-Kadi, O.S. (2015). A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours. Comput. Med. Imaging Graph. 41: 67–79, https://doi.org/10.1016/j.compmedimag.2014.05.013.Search in Google Scholar PubMed

Al-Kadi, O.S. (2017). A Gabor filter texture analysis approach for histopathological brain tumor subtype discrimination, Available at: https://arxiv.org/pdf/ 1704.05122.pdf.Search in Google Scholar

Al-Kadi, O.S. and Diaz, O. (2023). Editorial: reviews in cancer imaging and image-directed interventions. Front. Oncol. 13: 1183302, https://doi.org/10.3389/fonc.2023.1183302.Search in Google Scholar PubMed PubMed Central

Al-Kadi, O.S. and Di Ieva, A. (2016). Histological fractal-based classification of brain tumors. In: Di Ieva, A. (Ed.). The fractal geometry of the brain. Springer, New York, NY., pp. 371–391.10.1007/978-1-4939-3995-4_23Search in Google Scholar

Al-Kadi, O.S. and Watson, D. (2008). Texture analysis of aggressive and nonaggressive lung tumor CE CT images. IEEE. Trans. Biomed. Eng. 55: 1822–1830, https://doi.org/10.1109/tbme.2008.919735.Search in Google Scholar

Amiri, S., Rekik, I., and Mahjoub, M.A. (2016). Deep random forest-based learning transfer to SVM for brain tumor segmentation. In: 2016 2nd international conference on advanced technologies for signal and image processing. IEEE, Monastir, Tunisia, pp. 297–302.10.1109/ATSIP.2016.7523095Search in Google Scholar

Ammari, S., Sallé de Chou, R., Balleyguier, C., Chouzenoux, E., Touat, M., Quillent, A., Dumont, S., Bockel, S., Garcia, G.C.T.E., Elhaik, M., et al.. (2021). A predictive clinical-radiomics nomogram for survival prediction of glioblastoma using MRI. Diagnostics 11: 2043, https://doi.org/10.3390/diagnostics11112043.Search in Google Scholar PubMed PubMed Central

Ardila, D., Kiraly, A.P., Bharadwaj, S., Choi, B., Reicher, J.J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al.. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25, Article 6, https://doi.org/10.1038/s41591-019-0447-x.Search in Google Scholar PubMed

Bahadure, N.B., Ray, A.K., and Thethi, H.P. (2017). Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int. J. Biomed. Imaging, 2017, 9749108, https://doi.org/10.1155/2017/9749108.Search in Google Scholar PubMed PubMed Central

Ballantyne, A. and Stewart, C. (2019). Big data and public-private partnerships in healthcare and research. Asian Bioeth. Rev. 11: 315–326, https://doi.org/10.1007/s41649-019-00100-7.Search in Google Scholar PubMed PubMed Central

Banerjee, M., Chiew, D., Patel, K.T., Johns, I., Chappell, D., Linton, N., Cole, G.D., Francis, D.P., Szram, J., Ross, J., et al.. (2021). The impact of artificial intelligence on clinical education: perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers. BMC Med. Educ. 21: 429, https://doi.org/10.1186/s12909-021-02870-x.Search in Google Scholar PubMed PubMed Central

Barber, J., Yuen, J., Jameson, M., Schmidt, L., Sykes, J., Gray, A., Hardcastle, N., Choong, C., Poder, J., Walker, A., et al.. (2020). Deforming to best practice: key considerations for deformable image registration in radiotherapy. J. Med. Radiat. 67: 318–332, https://doi.org/10.1002/jmrs.417.Search in Google Scholar PubMed PubMed Central

Barragán-Montero, A., Javaid, U., Valdés, G., Nguyen, D., Desbordes, P., Macq, B., Willems, S., Vandewinckele, L., Holmström, M., Löfman, F., et al.. (2021). Artificial intelligence and machine learning for medical imaging: a technology review. Phys. Med. 83: 242–256, https://doi.org/10.1016/j.ejmp.2021.04.016.Search in Google Scholar PubMed PubMed Central

Bashiri, F.S., Baghaie, A., Rostami, R., Yu, Z., and D’Souza, R.M. (2018). Multi-modal medical image registration with full or partial data: a manifold learning approach. J. Imaging 5: 5, https://doi.org/10.3390/jimaging5010005.Search in Google Scholar PubMed PubMed Central

Ben Naceur, M., Akil, M., Saouli, R., and Kachouri, R. (2020). Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy. Med. Image Anal. 63: 101692, https://doi.org/10.1016/j.media.2020.101692.Search in Google Scholar PubMed

Bezdek, J.C., Hall, L.O., and Clarke, L.P. (1993). Review of MR image segmentation techniques using pattern recognition. J. Med. Phys. 20: 1033–1048, https://doi.org/10.1118/1.597000.Search in Google Scholar PubMed

Biratu, E.S., Schwenker, F., Ayano, Y.M., and Debelee, T.G. (2021). A survey of brain tumor segmentation and classification algorithms. J. Imaging 7: 179, https://doi.org/10.3390/jimaging7090179.Search in Google Scholar PubMed PubMed Central

Breiman, L. (2001). Random Forests. Mach. Learn. 45: 5–32, https://doi.org/10.1023/a:1010933404324.10.1023/A:1010933404324Search in Google Scholar

Bugeja, J.M., Mehawed, G., Roberts, M.J., Rukin, N., Dowling, J., and Murray, R. (2023). Prostate volume analysis in image registration for prostate cancer care: a verification study. Phys. Eng. Sci. Med. 46: 1791–1802, https://doi.org/10.1007/s13246-023-01342-4.Search in Google Scholar PubMed PubMed Central

Calabrese, E., Villanueva-Meyer, J.E., and Cha, S. (2020). A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas. Sci. Rep. 10, Article 1, https://doi.org/10.1038/s41598-020-68857-8.Search in Google Scholar PubMed PubMed Central

Cao, X., Yang, J., Zhang, J., Wang, Q., Yap, P.-T., and Shen, D. (2018). Deformable image registration using a cue-aware deep regression network. IEEE. Trans. Biomed. Eng. 65: 1900–1911, https://doi.org/10.1109/tbme.2018.2822826.Search in Google Scholar PubMed PubMed Central

Davnall, F., Yip, C. S., Ljungqvist, G., Selmi, M., Ng, F., Sanghera, B., Ganeshan, B., Miles, K. A., Cook, GJ., and Goh, V. (2012). Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging 3: 573–589.10.1007/s13244-012-0196-6Search in Google Scholar PubMed PubMed Central

Depeursinge, A., Al-Kadi, O., and Mitchell, J. (2017). Biomedical texture analysis: Fundamentals, Tools and challenges. Academic Press, London, UK.Search in Google Scholar

Di Salle, G., Fanni, S. C., Aghakhanyan, G., and Neri, E. (2023). Current applications of AI in medical imaging. In: Introduction to Artificial Intelligence. Springer International Publishing, pp. 151–165.10.1007/978-3-031-25928-9_8Search in Google Scholar

Diakogiannis, F.I., Waldner, F., Caccetta, P., and Wu, C. (2020). ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J. Photogramm. Remote Sens. 162: 94–114, https://doi.org/10.1016/j.isprsjprs.2020.01.013.Search in Google Scholar

Díaz-Pernas, F.J., Martínez-Zarzuela, M., Antón-Rodríguez, M., and González-Ortega, D. (2021). A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. Healthcare 9: 153, https://doi.org/10.3390/healthcare9020153.Search in Google Scholar PubMed PubMed Central

Di Franco, G. and Santurro, M. (2021). Machine learning, artificial neural networks and social research. Qual. Quant. 55: 1007–1025, https://doi.org/10.1007/s11135-020-01037-y.Search in Google Scholar

Di Ieva, A. and Al-Kadi, O.S. (2016). Computational fractal-based analysis of brain tumor microvascular networks. In: Di Ieva, A. (Ed.). The fractal geometry of the brain. Springer, New York, NY, pp. 393–411.10.1007/978-1-4939-3995-4_24Search in Google Scholar

Dilruba, R.A., Chowdhury, N., Liza, F.F., and Karmakar, C.K. (2006). Data pattern recognition using neural network with back-propagation training. In: 2006 International conference on electrical and computer engineering. IEEE, Dhaka, Bangladesh, pp. 451–455.10.1109/ICECE.2006.355667Search in Google Scholar

Erdag, N., Bhorade, R.M., Alberico, R.A., Yousuf, N., and Patel, M.R. (2001). Primary lymphoma of the central nervous system. AJR, Am. J. Roentgenol. 176: 1319–1326, https://doi.org/10.2214/ajr.176.5.1761319.Search in Google Scholar PubMed

Estienne, T., Lerousseau, M., Vakalopoulou, M., Alvarez Andres, E., Battistella, E., Carré, A., Chandra, S., Christodoulidis, S., Sahasrabudhe, M., Sun, R., et al.. (2020). Deep learning-based concurrent brain registration and tumor segmentation. Front. Comput. Neurosci. 14: 17, https://doi.org/10.3389/fncom.2020.00017.Search in Google Scholar PubMed PubMed Central

Fan, J., Cao, X., Yap, P.-T., and Shen, D. (2019). BIRNet: brain image registration using dual-supervised fully convolutional networks. Med. Image Anal. 54: 193–206, https://doi.org/10.1016/j.media.2019.03.006.Search in Google Scholar PubMed PubMed Central

Fu, Y., Lei, Y., Wang, T., Curran, W.J., Liu, T., and Yang, X. (2020). Deep learning in medical image registration: a review. Phys. Med. Biol. 65: 20TR01, https://doi.org/10.1088/1361-6560/ab843e.Search in Google Scholar PubMed PubMed Central

Gaillard, F., Sharma, R., Atkinson, H., et al.. Phakomatoses. Radiology Reference Article, Radiopaedia.org. Available at: https://doi.org/10.53347/rID-8212.Search in Google Scholar

Galván, E. and Mooney, P. (2021). Neuroevolution in deep neural networks: current trends and future challenges. IEEE Trans. Artif. Intell. 2: 476–493, https://doi.org/10.1109/tai.2021.3067574.Search in Google Scholar

Goetz, M., Weber, C., Binczyk, F., Polanska, J., Tarnawski, R., Bobek-Billewicz, B., Koethe, U., Kleesiek, J., Stieltjes, B., and Maier-Hein, K.H. (2016). DALSA: domain adaptation for supervised learning from sparsely annotated MR images. IEEE Trans. Med. Imag. 35: 184–196, https://doi.org/10.1109/tmi.2015.2463078.Search in Google Scholar

Gordillo, N., Montseny, E., and Sobrevilla, P. (2013). State of the art survey on MRI brain tumor segmentation. Magn. Reson. Imaging 31: 1426–1438, https://doi.org/10.1016/j.mri.2013.05.002.Search in Google Scholar PubMed

Ilunga-Mbuyamba, E., Cruz-Duarte, J.M., Avina-Cervantes, J.G., Correa-Cely, C.R., Lindner, D., and Chalopin, C. (2016). Active contours driven by Cuckoo Search strategy for brain tumour images segmentation. Expert Syst. Appl. 56: 59–68, https://doi.org/10.1016/j.eswa.2016.02.048.Search in Google Scholar

Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., and Maier-Hein, K.H. (2021). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, Article 2, https://doi.org/10.1038/s41592-020-01008-z.Search in Google Scholar PubMed

Işın, A., Direkoğlu, C., and Şah, M. (2016). Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput. Sci. 102: 317–324, https://doi.org/10.1016/j.procs.2016.09.407.Search in Google Scholar

Islam, K.T., Wijewickrema, S., and O’Leary, S. (2021). A deep learning based framework for the registration of three dimensional multi-modal medical images of the head. Sci. Rep. 11, Article 1, https://doi.org/10.1038/s41598-021-81044-7.Search in Google Scholar PubMed PubMed Central

Jayakumar, S., Sounderajah, V., Normahani, P., Harling, L., Markar, S.R., Ashrafian, H., and Darzi, A. (2022). Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study. Npj Digit. Med. 5, Article 1, https://doi.org/10.1038/s41746-021-00544-y.Search in Google Scholar PubMed PubMed Central

Jiji, G. and Ganesan, L. (2007). Unsupervised segmentation using Fuzzy logic based texture spectrum for MRI brain images. WEC, Istanbul, Turkey.Search in Google Scholar

Jönsson, H., Ekström, S., Strand, R., Pedersen, M.A., Molin, D., Ahlström, H., and Kullberg, J. (2022). An image registration method for voxel-wise analysis of whole-body oncological PET-CT. Sci. Rep. 12, Article 1, https://doi.org/10.1038/s41598-022-23361-z.Search in Google Scholar PubMed PubMed Central

Joseph, R.P., Singh, C.S., and Manikandan, M. (2014). Brain tumor MRI image segmentation and detection in image processing. Int. Res. J. Eng. Technol. 3: 1–5, https://doi.org/10.15623/ijret.2014.0313001.Search in Google Scholar

Khan, F., Ayoub, S., Gulzar, Y., Majid, M., Reegu, F.A., Mir, M.S., Soomro, A.B., and Elwasila, O. (2023). MRI-based effective ensemble frameworks for predicting human brain tumor. J. Imaging 9: 163, https://doi.org/10.3390/jimaging9080163.Search in Google Scholar PubMed PubMed Central

Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., and Krishnan, D. (2020). Supervised contrastive learning. Adv. Neural Inf. Process. 33: 18661–18673.Search in Google Scholar

Kondylakis, H., Kalokyri, V., Sfakianakis, S., Marias, K., Tsiknakis, M., Jimenez-Pastor, A., Camacho-Ramos, E., Blanquer, I., Segrelles, J.D., López-Huguet, S., et al.. (2023). Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects. Eur. Radiol. Exp. 7: 20, https://doi.org/10.1186/s41747-023-00336-x.Search in Google Scholar PubMed PubMed Central

Koohi-Moghadam, M. and Bae, K.T. (2023). Generative AI in medical imaging: applications, challenges, and ethics. J. Med. Syst. 47: 94, https://doi.org/10.1007/s10916-023-01987-4.Search in Google Scholar PubMed

Kumar, A. (2023). Study and analysis of different segmentation methods for brain tumor MRI application. Multimed. Tools Appl. 82: 7117–7139, https://doi.org/10.1007/s11042-022-13636-y.Search in Google Scholar PubMed PubMed Central

Kumar, T.S., Rashmi, K., Ramadoss, S., Sandhya, L.K., and Sangeetha, T.J. (2017). Brain tumor detection using SVM classifier. In: 2017 Third international conference on sensing. Signal Processing and Security, pp. 318–323.10.1109/SSPS.2017.8071613Search in Google Scholar

Lambin, P., Leijenaar, R.T.H., Deist, T.M., Peerlings, J., de Jong, E.E.C., van Timmeren, J., Sanduleanu, S., Larue, R.T.H.M., Even, A.J.G., Jochems, A., et al.. (2017). Radiomics: the bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14: 749–762, https://doi.org/10.1038/nrclinonc.2017.141.Search in Google Scholar PubMed

Latif, G., Ben Brahim, G., Iskandar, D.N.F.A., Bashar, A., and Alghazo, J. (2022). Glioma tumors’ classification using deep-neural-network-based features with SVM classifier. Diagnostics 12: 1018, https://doi.org/10.3390/diagnostics12041018.Search in Google Scholar PubMed PubMed Central

Lefkovits, L., Lefkovits, S., and Szilágyi, L. (2016). Brain tumor segmentation with optimized random forest. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Winzeck, S., and Handels, H. (Eds.). Brainlesion: glioma, multiple sclerosis, Stroke and traumatic brain injuries. Springer, Athens, Greece, pp. 88–99.10.1007/978-3-319-55524-9_9Search in Google Scholar

Lei, Y., Dong, X., Tian, Z., Liu, Y., Tian, S., Wang, T., Jiang, X., Patel, P., Jani, A.B., Mao, H., et al.. (2020). CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network. Med. Phys. 47: 530–540, https://doi.org/10.1002/mp.13933.Search in Google Scholar PubMed PubMed Central

Lei, Y., Harms, J., Wang, T., Liu, Y., Shu, H.-K., Jani, A.B., Curran, W.J., Mao, H., Liu, T., and Yang, X. (2019). MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks. Med. Phys. 46: 3565–3581, https://doi.org/10.1002/mp.13617.Search in Google Scholar PubMed PubMed Central

Li, S. and Zhou, B. (2022). A review of radiomics and genomics applications in cancers: the way towards precision medicine. Radiat. Oncol. 17: 217, https://doi.org/10.1186/s13014-022-02192-2.Search in Google Scholar PubMed PubMed Central

Liu, J., Li, M., Wang, J., Wu, F., Liu, T., and Pan, Y. (2014). A survey of MRI-based brain tumor segmentation methods. Tsinghua Sci. Technol. 19: 578–595, https://doi.org/10.1109/tst.2014.6961028.Search in Google Scholar

Lok, K.H., Shi, L., Zhu, X., and Wang, D. (2017). Fast and robust brain tumor segmentation using level set method with multiple image information. J. X-Ray Sci. Technol. 25: 301–312, https://doi.org/10.3233/xst-17261.Search in Google Scholar PubMed

Louis, D.N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W.K., Ohgaki, H., Wiestler, O.D., Kleihues, P., and Ellison, D.W. (2016). The 2016 World health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 131: 803–820, https://doi.org/10.1007/s00401-016-1545-1.Search in Google Scholar PubMed

Louis, D.N., Perry, A., Wesseling, P., Brat, D.J., Cree, I.A., Figarella-Branger, D., Hawkins, C., Ng, H.K., Pfister, S.M., Reifenberger, G., et al.. (2021). The 2021 WHO classification of tumors of the central nervous system: a summary. J. Neurooncol. 23: 1231–1251, https://doi.org/10.1093/neuonc/noab106.Search in Google Scholar PubMed PubMed Central

Lundervold, A.S. and Lundervold, A. (2019). An overview of deep learning in medical imaging focusing on MRI. J. Med. Phys. 29: 102–127, https://doi.org/10.1016/j.zemedi.2018.11.002.Search in Google Scholar PubMed

Marcinkevičs, R., Ozkan, E., and Vogt, J.E. (2022). Introduction to machine learning for physicians: a survival guide for data deluge, Available at: https:// arxiv.org/pdf/2212.12303.pdf.Search in Google Scholar

Marias, K. (2021). The constantly evolving role of medical image processing in oncology: from traditional medical image processing to imaging biomarkers and radiomics. J. Imaging 7: 124, https://doi.org/10.3390/jimaging7080124.Search in Google Scholar PubMed PubMed Central

McKinley, R., Meier, R., and Wiest, R. (2019). Ensembles of densely-connected CNNs with label-uncertainty for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., and van Walsum, T. (Eds.). Brainlesion: glioma, multiple sclerosis, Stroke and traumatic brain injuries. Springer, Shenzhen, China, pp. 456–465.10.1007/978-3-030-11726-9_40Search in Google Scholar

Meaney, C., Das, S., Colak, E., and Kohandel, M. (2023). Deep learning characterization of brain tumours with diffusion weighted imaging. J. Theor. Biol. 557: 111342, https://doi.org/10.1016/j.jtbi.2022.111342.Search in Google Scholar PubMed

Meier, R., Knecht, U., Loosli, T., Bauer, S., Slotboom, J., Wiest, R., and Reyes, M. (2016). Clinical evaluation of a fully-automatic segmentation method for longitudinal brain tumor volumetry. Sci. Rep. 6: 23376, https://doi.org/10.1038/srep23376.Search in Google Scholar PubMed PubMed Central

Mitchell, T.M. (1997). Machine learning. McGraw-Hill, New York, NY.Search in Google Scholar

Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J.N.L., and Išgum, I. (2016). Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. 35: 1252–1261, https://doi.org/10.1109/tmi.2016.2548501.Search in Google Scholar PubMed

Mohammed, Y.M.A., El Garouani, S., and Jellouli, I. (2023). A survey of methods for brain tumor segmentation-based MRI images. J. Comput. Des. Eng. 10: 266–293, https://doi.org/10.1093/jcde/qwac141.Search in Google Scholar

Mohseni Salehi, S.S., Khan, S., Erdogmus, D., and Gholipour, A. (2019). Real-time deep pose estimation with geodesic loss for image-to-template rigid registration. IEEE Trans. Med. 38: 470–481, https://doi.org/10.1109/tmi.2018.2866442.Search in Google Scholar

Molina-García, D., Vera-Ramírez, L., Pérez-Beteta, J., Arana, E., and Pérez-García, V.M. (2019). Prognostic models based on imaging findings in glioblastoma: human versus Machine. Sci. Rep. 9: 5982, https://doi.org/10.1038/s41598-019-42326-3.Search in Google Scholar PubMed PubMed Central

Murdoch, B. (2021). Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med. Ethics 22: 122, https://doi.org/10.1186/s12910-021-00687-3.Search in Google Scholar PubMed PubMed Central

Mzoughi, H., Njeh, I., Wali, A., Slima, M.B., BenHamida, A., Mhiri, C., and Mahfoudhe, K.B. (2020). Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification. J. Digit. Imaging 33: 903–915, https://doi.org/10.1007/s10278-020-00347-9.Search in Google Scholar PubMed PubMed Central

Öfverstedt, J. (2022) Methods for reliable image registration: algorithms, distance measures, and representations. PhD thesis, Uppsala, Acta Universitatis Upsaliensis.Search in Google Scholar

Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.. (2018). Attention U-Net: learning where to look for the pancreas, Available at: https:// arxiv.org/pdf/1804.03999.pdf.Search in Google Scholar

Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al.. (2020). AI in medical imaging informatics: current challenges and future directions. IEEE J. Biomed. Health Informatics 24: 1837–1857.10.1109/JBHI.2020.2991043Search in Google Scholar PubMed PubMed Central

Pesapane, F., Rotili, A., Agazzi, G.M., Botta, F., Raimondi, S., Penco, S., Dominelli, V., Cremonesi, M., Jereczek-Fossa, B.A., Carrafiello, G., et al.. (2021). Recent radiomics advancements in breast cancer: lessons and pitfalls for the next future. Curr. Oncol. 28: 2351–2372, https://doi.org/10.3390/curroncol28040217.Search in Google Scholar PubMed PubMed Central

Popuri, K., Cobzas, D., Murtha, A., and Jägersand, M. (2012). 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set. Int. J. Comput. Assist. Radiol. Surg. 7: 493–506, https://doi.org/10.1007/s11548-011-0649-2.Search in Google Scholar PubMed

Prince, J.L. and McVeigh, E.R. (1991). Optical flow for tagged MR images. In: Proceedings of international conference on acoustics, speech, and signal processing. IEEE, Toronto, Canada., pp. 2441–2444.10.1109/ICASSP.1991.150894Search in Google Scholar

Rafiee, H. (2019). Chapman and Nakielny’s aids to radiological differential diagnosis. Elsevier Health Sciences, London, UK.Search in Google Scholar

Rajendran, A. and Dhanasekaran, R. (2012). Fuzzy clustering and deformable model for tumor segmentation on MRI brain image: a combined approach. Procedia Eng. 30: 327–333, https://doi.org/10.1016/j.proeng.2012.01.868.Search in Google Scholar

Rajesh Babu, K., Naganjaneyulu, P.V., and Prasad, K.S. (2021). Comparative analysis of active contour models for brain tumor segmentation from T1w MRI images. In: 2021 International Conference on Computer Communication and Informatics, pp. 1–6.Search in Google Scholar

Ranjbarzadeh, R., Caputo, A., Babaee Tirkolaee, E., Ghoushchi, S., and Bendechache, M. (2022). Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools. Comput. Biol. Med. 152: 106405, https://doi.org/10.1016/j.compbiomed.2022.106405.Search in Google Scholar PubMed

RANZCR (n.d.). Learning Outcomes and Handbook, Available at: https://www.ranzcr.com/trainees/clinical-radiology-training-program/learning-outcomes-and-handbook (Accessed 27 Nov 2023).Search in Google Scholar

Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A.I., Etmann, C., McCague, C., Beer, L., et al.. (2021). Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3, Article 3, https://doi.org/10.1038/s42256-021-00307-0.Search in Google Scholar

Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: convolutional networks for biomedical image segmentation, Available at: https://arxiv.org/pdf/ 1505.04597.pdf.10.1007/978-3-319-24574-4_28Search in Google Scholar

Rosenman, J.G., Miller, E.P., Tracton, G., and Cullip, T.J. (1998). Image registration: an essential part of radiation therapy treatment planning. Int. J. Radiat. Oncol. Biol. Phys. 40: 197–205, https://doi.org/10.1016/s0360-3016(97)00546-4.Search in Google Scholar PubMed

Sabour, S., Frosst, N., and Hinton, G.E. (2017). Dynamic routing between capsules, Available at: https://arxiv.org/pdf/1710.09829.pdf.Search in Google Scholar

Saidani, O., Aljrees, T., Umer, M., Alturki, N., Alshardan, A., Khan, S.W., Alsubai, S., and Ashraf, I. (2023). Enhancing prediction of brain tumor classification using images and numerical data features. Diagnostics 13: 2544, https://doi.org/10.3390/diagnostics13152544.Search in Google Scholar PubMed PubMed Central

Saman, S. and Jamjala Narayanan, S. (2019). Survey on brain tumor segmentation and feature extraction of MR images. Int. J. Multimed. Inf. Retr. 8: 79–99, https://doi.org/10.1007/s13735-018-0162-2.Search in Google Scholar

Saman, S. and Narayanan, S.J. (2021). Active contour model driven by optimized energy functionals for MR brain tumor segmentation with intensity inhomogeneity correction. Multimed. Tools Appl. 80: 21925–21954, https://doi.org/10.1007/s11042-021-10738-x.Search in Google Scholar

Sarwar, I., Asghar, M., and Naeem, M.A. (2017). Learning-based improved seeded region growing algorithm for brain tumor identification. Proc. Pak. Acad. 54: 127–133.Search in Google Scholar

Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. D 404: 132306, https://doi.org/10.1016/j.physd.2019.132306.Search in Google Scholar

Shim, K.Y., Chung, S.W., Jeong, J.H., Hwang, I., Park, C.-K., Kim, T.M., Park, S.-H., Won, J.K., Lee, J.H., Lee, S.-T., et al.. (2021). Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI. Sci. Rep. 11: 9974, https://doi.org/10.1038/s41598-021-89218-z.Search in Google Scholar PubMed PubMed Central

Soltaninejad, M., Yang, G., Lambrou, T., Allinson, N., Jones, T.L., Barrick, T.R., Howe, F.A., and Ye, X. (2018). Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput. Methods Programs Biomed. 157: 69–84, https://doi.org/10.1016/j.cmpb.2018.01.003.Search in Google Scholar PubMed

Soltaninejad, M., Zhang, L., Lambrou, T., Allinson, N., and Ye, X. (2017). Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network, Available at: https://arxiv.org/pdf/1704.08134.pdf.Search in Google Scholar

Sujan, Md., Alam, N., Abdullah, S., and Jahirul, M. (2016). A segmentation based automated system for brain tumor detection. Int. J. Comput. Appl. 153: 41–49, https://doi.org/10.5120/ijca2016912177.Search in Google Scholar

Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., and Bray, F. (2021). Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: CA Cancer J. Clin., 71: 209–249.10.3322/caac.21660Search in Google Scholar PubMed

Suthaharan, S. (2016). Support vector machine. In: Suthaharan, S. (Ed.). Machine learning models and algorithms for Big data classification: thinking with examples for effective learning. Springer, New York, NY, pp. 207–235.10.1007/978-1-4899-7641-3_9Search in Google Scholar

Thaha, M.M., Kumar, K.P.M., Murugan, B.S., Dhanasekeran, S., Vijayakarthick, P., and Selvi, A.S. (2019). Brain tumor segmentation using convolutional neural networks in MRI images. J. Med. Syst. 43: 294, https://doi.org/10.1007/s10916-019-1416-0.Search in Google Scholar PubMed

The Radiology Assistant. (n.d.). Systematic approach, Avaliable at: https://radiologyassistant.nl/neuroradiology/brain-tumor/systematic-approach (Accessed 24 June 2023).Search in Google Scholar

Thias, A.H., Al Mubarok, A.F., Handayani, A., Danudirdjo, D., and Rajab, T.E. (2019). Brain tumor semi-automatic segmentation on MRI T1-weighted images using active contour models. In: 2019 International conference on mechatronics, robotics and systems engineering (MoRSE). IEEE, Bali, Indonesia, pp. 217–221.10.1109/MoRSE48060.2019.8998651Search in Google Scholar

Topol, E.J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, Article 1, https://doi.org/10.1038/s41591-018-0300-7.Search in Google Scholar PubMed

Vadhnani, S. and Singh, N. (2022). Brain tumor segmentation and classification in MRI using SVM and its variants: a survey. Multimed. Tools Appl. 81: 31631–31656, https://doi.org/10.1007/s11042-022-12240-4.Search in Google Scholar

Veeramuthu, A., Meenakshi, S., Mathivanan, G., Kotecha, K., Saini, J. R., Vijayakumar, V., and Subramaniyaswamy, V. (2022). MRI brain tumor image classification using a combined feature and image-based classifier. Front. Psychol. 13: 848784, https://doi.org/10.3389/fpsyg.2022.848784.Search in Google Scholar PubMed PubMed Central

Vijay, J. and Subhashini, J. (2013). An efficient brain tumor detection methodology using K-means clustering algoriftnn. In: 2013 International conference on communication and signal processing. IEEE, Melmaruvathur, India, pp. 653–657.10.1109/iccsp.2013.6577136Search in Google Scholar

Wadhwa, A., Bhardwaj, A., and Singh Verma, V. (2019). A review on brain tumor segmentation of MRI images. Magn. Reson. Imaging 61: 247–259, https://doi.org/10.1016/j.mri.2019.05.043.Search in Google Scholar PubMed

Wang, S., Zhang, Y., Dong, Z., Du, S., Ji, G., Yan, J., Yang, J., Wang, Q., Feng, C., and Phillips, P. (2015). Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int. J. Imaging Syst. Technol. 25: 153–164, https://doi.org/10.1002/ima.22132.Search in Google Scholar

Willemink, M.J., Roth, H.R., and Sandfort, V. (2022). Toward foundational deep learning models for medical imaging in the new era of transformer networks. Radiol.: Artif. Intell. 4: e210284, https://doi.org/10.1148/ryai.210284.Search in Google Scholar PubMed PubMed Central

Wong, K.-P. (2005). Medical image segmentation: methods and applications in functional imaging. In: Suri, J.S., Wilson, D.L., and Laxminarayan, S. (Eds.). Handbook of biomedical image analysis: volume II: segmentation models part B. Springer, Boston, MA, pp. 111–182.10.1007/0-306-48606-7_3Search in Google Scholar

Wu, X., Bi, L., Fulham, M., Feng, D.D., Zhou, L., and Kim, J. (2021). Unsupervised brain tumor segmentation using a symmetric-driven adversarial network. Neurocomputing 455: 242–254, https://doi.org/10.1016/j.neucom.2021.05.073.Search in Google Scholar

Wu, X., Liu, X., and Zhou, Y. (2022). Review of unsupervised learning techniques. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., and Zheng, S. (Eds.). Proceedings of 2021 Chinese intelligent systems conference. Springer, Singapore, pp. 576–590.10.1007/978-981-16-6324-6_59Search in Google Scholar

Xiao, C., Lei, Y., Ma, Y., Zhou, F., and Qin, Z. (2021). DeepSeg: deep-learning-based activity segmentation framework for activity recognition using WiFi. IEEE Internet Things J. 8: 5669–5681, https://doi.org/10.1109/jiot.2020.3033173.Search in Google Scholar

Xie, X., Song, Y., Ye, F., Yan, H., Wang, S., Zhao, X., and Dai, J. (2022). The application of multiple metrics in deformable image registration for target volume delineation of breast tumor bed. J. Appl. Clin. Med. Phys. 23: e13793, https://doi.org/10.1002/acm2.13793.Search in Google Scholar PubMed PubMed Central

Xu, C., Yezzi, A., and Prince, J.L. (2000). On the relationship between parametric and geometric active contours. In: Conference record of the thirty-fourth asilomar conference on signals, systems and computers (Cat. No. 00CH37154), 1. IEEE, Pacific Grove, USA, pp. 483–489.Search in Google Scholar

Zeineldin, R.A., Karar, M.E., Burgert, O., and Mathis-Ullrich, F. (2022). Multimodal CNN networks for brain tumor segmentation in MRI: a BraTS 2022 challenge solution, Available at: https://arxiv.org/pdf/2212.09310.pdf.10.1007/978-3-031-33842-7_11Search in Google Scholar

Zhao, L., Ma, J., Shao, Y., Jia, C., Zhao, J., and Yuan, H. (2022). MM-UNet: a multimodality brain tumor segmentation network in MRI images. Front. Oncol. 12, https://doi.org/10.3389/fonc.2022.950706.Search in Google Scholar PubMed PubMed Central

Zou, J., Gao, B., Song, Y., and Qin, J. (2022). A review of deep learning-based deformable medical image registration. Front. Oncol. 12: 950706, 1047215, https://doi.org/10.3389/fonc.2022.1047215.Search in Google Scholar PubMed PubMed Central