Abstract
With time, AI technologies have matured well and resonated in various domains of applied sciences and engineering. The sub-domains of AI, machine learning (ML), deep learning (DL), and associated statistical tools are getting more attention. Therefore, various machine learning models are being created to take advantage of the data available and accomplish tasks, such as automatic prediction, classification, clustering, segmentation and anomaly detection, etc. Tasks like classification need labeled data used to train the models to achieve a reliable accuracy. This study shows the systematic review of promising research areas and applications of DL models in medical diagnosis and medical healthcare systems. The prevalent DL models, their architectures, and related pros, cons are discussed to clarify their prospects. Many deep learning networks have been useful in the field of medical image processing for prognosis and diagnosis of life-threatening ailments (e.g., breast cancer, lung cancer, and brain tumor, etc.), which stand as an error-prone and tedious task for doctors and specialists when performed manually. Medical images are processed using these DL methods to solve various tasks like prediction, segmentation, and classification with accuracy bypassing human abilities. However, the current DL models have some limitations that encourage the researchers to seek further improvement.
Similar content being viewed by others
References
Xi, X., Meng, X., Yang, L., Nie, X., Yang, G., Chen, H., Fan, X., Yin, Y., Chen, X.: Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior. Multimed. Syst. 25(2), 95–102 (2019)
Salakhutdinov, R., Hinton, G.: (2009, April) Deep boltzmann machines. International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, Clearwater Beach, Florida, USA. Volume 5 of JMLR: W&CP 5. (pp. 448-455)
Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning-a new frontier in artificial intelligence research. IEEE Comput. Intell. Mag. 5(4), 13–18 (2010)
Ahmed, M., Shill, P.C., Islam, K., Mollah, M.A., Akhand, M.A.: Acoustic modeling using deep belief network for Bangla speech recognition. In: 2015 18th International Conference on Computer and Information Technology (ICCIT), pp. 306–311. IEEE (2015)
Zou, Y., Jin, X., Li, Y., Guo, Z., Wang, E., Xiao, B.: Mariana: Tencent deep learning platform and its applications. Proc. VLDB Endow. 7(13), 1772–1777 (2014)
Cireşan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Deep, big, simple neural nets for handwritten digit recognition. Neural Comput. 22(12), 3207–3220 (2010)
Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 513–520 (2011)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Bengio, Y., Courville, A.C., Vincent, P.: Unsupervised feature learning and deep learning: a review and new perspectives. CoRR. arXiv:abs/1206.5538 (2012)
Zhang, J., Zhou, Y., Xia, K., Jiang, Y., Liu, Y.: A novel automatic image segmentation method for Chinese literati paintings using multi-view fuzzy clustering technology. Multimed. Syst. 26(1), 37–51 (2020)
Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649. IEEE (2013)
Huang, F.J., LeCun, Y.: Large-scale learning with SVM and convolutional nets for generic object categorization. In: Proceedings of Computer Vision and Pattern Recognition Conference (CVPR’06) (2006)
Kwolek, B.: Face detection using convolutional neural networks and Gabor filters. In: International Conference on Artificial Neural Networks, pp. 551–556. Springer, Berlin, Heidelberg (2005)
Sukittanon, S., Surendran, A.C., Platt, J.C., Burges, C.J.: Convolutional networks for speech detection. In: Eighth International Conference on Spoken Language Processing (2004)
Chen, Y.N., Han, C.C., Wang, C.T., Jeng, B.S., Fan, K.C.: The application of a convolution neural network on face and license plate detection. In: 18th International Conference on Pattern Recognition (ICPR’06), vol. 3, pp. 552–555. IEEE (2006)
Rizk, Y., Hajj, N., Mitri, N., Awad, M.: Deep belief networks and cortical algorithms: A comparative study for supervised classification. Applied Computing and Informatics 15(2), 81–93 (2019)
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., Lang, K.J.: Phoneme recognition using time-delay neural networks. Backpropagation: theory, architectures and applications. 35–61 (1995)
Lang, K.J., Waibel, A.H., Hinton, G.E.: A time-delay neural network architecture for isolated word recognition. Neural Netw. 3(1), 23–43 (1990)
Hadsell, R., Erkan, A., Sermanet, P., Scoffier, M., Muller, U., LeCun, Y.: Deep belief net learning in a long-range vision system for autonomous off-road driving. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 628–633. IEEE (2008)
Marcus M, Santorini B, Marcinkiewicz MA. Building a large annotated corpus of English: the Penn Treebank
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 215–223 (2011)
Guyon, I., Dror, G., Lemaire, V., Taylor, G., Aha, D.W.: Unsupervised and transfer learning challenge. In: The 2011 International Joint Conference on Neural Networks, pp. 793–800. IEEE (2011)
Rajpurohit, S., Patil, S., Choudhary, N., Gavasane, S., Kosamkar, P.: Identification of acute lymphoblastic leukemia in microscopic blood image using image processing and machine learning algorithms. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2359–2363. IEEE (2018)
Greenspan, H., Van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique. IEEE Transactions on Medical Imaging 35(5), 1153–1159 (2016)
Litjens, G., Sánchez, C.I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen-Van De Kaa, C., Bult, P., Van Ginneken, B., Van Der Laak, J.: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016)
Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., Summers, R.M.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Suzuki, K.: Overview of deep learning in medical imaging. Radiol. Phys. Technol. 10(3), 257–273 (2017)
Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221–248 (2017)
Lee, J.G., Jun, S., Cho, Y.W., Lee, H., Kim, G.B., Seo, J.B., Kim, N.: Deep learning in medical imaging: general overview. Korean J Radiol. 18(4), 570–584 (2017)
Suzuki, K.: Pixel-based machine learning in medical imaging. J. Biomed. Imaging 2012, 1 (2012)
Vargas, R., Mosavi, A., Ruiz, R.: Deep learning: a review. Advances in intelligent systems and computing. (2017)
Mamoshina, P., Vieira, A., Putin, E., Zhavoronkov, A.: Applications of deep learning in biomedicine. Mol. Pharm. 13(5), 1445–1454 (2016)
Cao, C., Liu, F., Tan, H., Song, D., Shu, W., Li, W., Zhou, Y., Bo, X., Xie, Z.: Deep learning and its applications in biomedicine. Genom. Proteom. Bioinform. 16(1), 17–32 (2018)
Khan, S., Islam, N., Jan, Z., Din, I.U., Rodrigues, J.J.: A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recogn. Lett. 125, 1–6 (2019)
Zhang, J., Xie, Y., Wu, Q., Xia, Y.: Medical image classification using synergic deep learning. Med. Image Anal. 54, 10–19 (2019)
Mahbod, A., Schaefer, G., Wang, C., Ecker, R., Ellinge, I.: Skin lesion classification using hybrid deep neural networks. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp. 1229–1233. IEEE (2019)
Xu, Y., Hosny, A., Zeleznik, R., Parmar, C., Coroller, T., Franco, I., Mak, R.H., Aerts, H.J.: Deep learning predicts lung cancer treatment response from serial medical imaging. Clin. Cancer Res. 25(11), 3266–3275 (2019)
Nagpal, K., Foote, D., Liu, Y., Chen, P.H., Wulczyn, E., Tan, F., Olson, N., Smith, J.L., Mohtashamian, A., Wren, J.H., Corrado, G.S.: Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit. Med. 2(1), 1 (2019)
Asuntha, A., Srinivasan, A.: Deep learning for lung Cancer detection and classification. Multimed Tools Appl 79, 7731–7762 (2020). https://doi.org/10.1007/s11042-019-08394-3
Ardila, D., Kiraly, A.P., Bharadwaj, S., Choi, B., Reicher, J.J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., Naidich, D.P.: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25(6), 954–961 (2019)
Benhammou, Y., Achchab, B., Herrera, F., Tabik, S.: BreakHis based breast cancer automatic diagnosis using deep learning: taxonomy, survey and insights. Neurocomputing. 375, 9–24 (2020)
Xie, H., Yang, D., Sun, N., Chen, Z., Zhang, Y.: Automated pulmonary nodule detection in CT images using deep convolutional neural networks. Pattern Recogn. 85, 109–119 (2019)
McBee, M.P., Awan, O.A., Colucci, A.T., Ghobadi, C.W., Kadom, N., Kansagra, A.P., Tridandapani, S., Auffermann, W.F.: Deep learning in radiology. Acad. Radiol. 25(11), 1472–1480 (2018)
Janssens, O., Van de Walle, R., Loccufier, M., Van Hoecke, S.: Deep learning for infrared thermal image based machine health monitoring. IEEE/ASME Trans. Mechatron. 23(1), 151–159 (2018)
Rajkomar, A., Oren, E., Chen, K., Dai, A.M., Hajaj, N., Hardt, M., Liu, P.J., Liu, X., Marcus, J., Sun, M., Sundberg, P.: Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1(1), 18 (2018)
Oh, S.L., Hagiwara, Y., Raghavendra, U., Yuvaraj, R., Arunkumar, N., Murugappan, M., Acharya, U.R.: A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput. Appl. 1–7 (2018)
Coudray, N., Ocampo, P.S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A.L., Razavian, N., Tsirigos, A.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559 (2018)
Mohamed, A.A., Berg, W.A., Peng, H., Luo, Y., Jankowitz, R.C., Wu, S.: A deep learning method for classifying mammographic breast density categories. Med. Phys. 45(1), 314–321 (2018)
Nguyen, L.D., Lin, D., Lin, Z., Cao, J.: Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5. IEEE (2018)
Xu, J., Li, C., Zhou, Y., Mou, L., Zheng, H., Wang, S.: Classifying mammographic breast density by residual learning. arXiv preprint arXiv:1809.10241 (2018)
Rehman, A., Abbas, N., Saba, T., Rahman, S.I., Mehmood, Z., Kolivand, H.: Classification of acute lymphoblastic leukemia using deep learning. Microsc. Res. Tech. 81(11), 1310–1317 (2018)
Mohsen, H., El-Dahshan, E.S., El-Horbaty, E.S., Salem, A.B.: Classification using deep learning neural networks for brain tumors. Future Comput. Inform. J. 3(1), 68–71 (2018)
Chaudhary, K., Poirion, O.B., Lu, L., Garmire, L.X.: Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 24(6), 1248–1259 (2018)
Saffari, N., Rashwan, H., Herrera, B., Romani, S., Arenas, M., Puig, D.: On improving breast density segmentation using conditional generative adversarial networks. Artif. Intell. Res. Dev. Curr. Chall. New Trends Appl. 308, 386 (2018)
Soriano, D., Aguilar, C., Ramirez-Morales, I., Tusa, E., Rivas, W., Pinta, M.: Mammogram classification schemes by using convolutional neural networks. In: International Conference on Technology Trends, pp. 71–85. Springer, Cham (2017)
Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adeli, H.: Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270–278 (2018)
Lakhani, P., Sundaram, B.: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2), 574–582 (2017)
Hassan, T.M., Elmogy, M., Sallam, E.S.: Diagnosis of focal liver diseases based on deep learning technique for ultrasound images. Arab. J. Sci. Eng. 42(8), 3127–3140 (2017)
Gardezi, S.J., Faye, I., Bornot, J.M., Kamel, N., Hussain, M.: Mammogram classification using dynamic time warping. Multimed. Tools Appl. 77(3), 3941–3962 (2018)
Kooi, T., Litjens, G., Van Ginneken, B., Gubern-Mérida, A., Sánchez, C.I., Mann, R., den Heeten, A., Karssemeijer, N.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303–312 (2017)
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)
Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)
Sirinukunwattana, K., Raza, S.E.A., Tsang, Y.W., Snead, D.R., Cree, I.A., Rajpoot, N.M.: Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016)
Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., Li, L.: Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci. Rep. 6, 27327 (2016)
Lévy, D., Jain, A.: Breast mass classification from mammograms using deep convolutional neural networks. arXiv preprint arXiv:1612.00542 (2016)
Cheng, J.Z., Ni, D., Chou, Y.H., Qin, J., Tiu, C.M., Chang, Y.C., Huang, C.S., Shen, D., Chen, C.M.: Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep. 6, 24454 (2016)
Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer. arXiv preprint arXiv:1606.05718 (2016)
Kallenberg, M., Petersen, K., Nielsen, M., Ng, A.Y., Diao, P., Igel, C., Vachon, C.M., Holland, K., Winkel, R.R., Karssemeijer, N., Lillholm, M.: Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016)
Arevalo, J., González, F.A., Ramos-Pollán, R., Oliveira, J.L., Lopez, M.A.: Convolutional neural networks for mammography mass lesion classification. In: 2015 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 797–800. IEEE (2015)
Dhungel, N., Carneiro, G., Bradley, A.P.: Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–8. IEEE (2015)
Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., Greenspan, H.: Chest pathology detection using deep learning with non-medical training. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 294–297. IEEE (2015)
Xu, Y., Mo, T., Feng, Q., Zhong, P., Lai, M., Eric, I., Chang, C.: Deep learning of feature representation with multiple instance learning for medical image analysis. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1626–1630. IEEE (2014)
Suk, H.I., Lee, S.W., Shen, D., Alzheimer’s Disease Neuroimaging Initiative: Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuro Image 101, 569–582 (2014)
Liao, S., Gao, Y., Oto, A., Shen, D.: Representation learning: a unified deep learning framework for automatic prostate MR segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 254–261. Springer, Berlin, Heidelberg (2013)
Ambeth Kumar, V.D., et al.: Exploration of an Innovative geometric parameter based on performance enhancement for foot print recognition. Journal of Intelligent & Fuzzy Systems 38(2), 2181–2196 (2020). https://doi.org/10.3233/jifs-190982
Suk, H.I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 583–590. Springer, Berlin, Heidelberg (2013)
Bellotti, R., De Carlo, F., Tangaro, S., Gargano, G., Maggipinto, G., Castellano, M., Massafra, R., Cascio, D., Fauci, F., Magro, R., Raso, G.: A completely automated CAD system for mass detection in a large mammographic database. Med. Phys. 33(8), 3066–3075 (2006)
Chollet, F. et al.: Keras (2015). https://www.keras.io. Accessed 20 Jan 2020
Deng, L.: Three classes of deep learning architectures and their applications: a tutorial survey. APSIPA Trans. Signal Inf. Process. (2012)
Yu, D., Deng, L.: Deep learning and its applications to signal and information processing [exploratory dsp]. IEEE Signal Process. Mag. 28(1), 145–154 (2011)
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 609–616. ACM (2009)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Deselaers, T., Hasan, S., Bender, O., Ney, H.: A deep learning approach to machine transliteration. In: Proceedings of the Fourth Workshop on Statistical Machine Translation, pp. 233–241. Association for Computational Linguistics (2009)
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–27 (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Vimal, V., Singh, T., Qamar, S., Nautiyal, B., Udham Singh, K., Kumar, A.: Artificial intelligence-based novel scheme for location area planning in cellular networks. Comput. Intell. (2020). https://doi.org/10.1111/coin.12371
Baldi, P.: Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML workshop on unsupervised and transfer learning, pp. 37–49 (2012)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Center Berkeley: Caffe (2016) [Online]. https://caffe.berkeleyvision.org/. Accessed 20 Jan 2020
Microsoft: Cntk (2016) [Online]. https://github.com/Microsoft/CNTK. Accessed 20 Jan 2020
Skymind: Deeplearning4j (2016) [Online]. https://deeplearning4j.org/. Accessed 20 Jan 2020
Google: Tensorflow (2016) [Online]. https://www.tensorflow.org/. Accessed 20 Jan 2020
Collobert, R., Bengio, S.: Svmtorch: support vector machines for large-scale regression problems. J Mach Learn Res 1(2), 143–160 (2001)
Liu, F., Chen, L., Lu, L., Ahmad, A., Jeon, G., Yang, X.: Medical image fusion method by using Laplacian pyramid and convolutional sparse representation. Online published in concurrency and computation: practice and experience. ISSN 1532-0626
Kumar, I., Bhadauria, H.S., Virmani, J., Thakur, S.: A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybern. Biomed. Eng. 37(1), 217–228 (2017)
Kumar, I., Bhadauria, H.S., Virmani, J., Thakur, S.: A hybrid hierarchical framework for classification of breast density using digitized film screen mammograms. Multimed. Tools Appl. 76(18), 18789–18813 (2017)
Jiang, L., Ye, S., Yang, X., Ma, X., Lu, L., Ahmad, A., Jeon, G.: An adaptive anchored neighborhood regression method for medical image enhancement. Multimed. Tools Appl. 79, 10533–10550 (2020)
Wei, S., Wu, W., Jeon, G., Ahmad, A., Yang, X.: Improving resolution of medical images with deep dense convolutional neural network. Concurr. Comput. Pract. Exp. 32(1), e5084 (2020)
Lee, S., Rajan, S., Jeon, G., Chang, J.-H., Dajani, H.R., Groza, V.Z.: Oscillometric blood pressure estimation by combining nonparametric bootstrap with Gaussian mixture model. Comput. Biol. Med. 85, 112–124 (2017)
Jiang, W., Yang, X., Wu, W., Liu, K., Ahmad, A., Sangaiah, A.K., Jeon, G.: Medical images fusion by using weighted least squares filter and sparse representation. Comput. Electr. Eng. 67, 252–266 (2018)
Kumar, I., Bhadauria, H.S., Virmani, J.: A computerised framework for prediction of fatty and dense breast tissue using principal component analysis and multi-resolution texture descriptors. Int. J. Comput. Syst. Eng. 4(2–3), 73–85 (2018)
Wang, F., Preininger, A.: AI in health: state of the art, challenges, and future directions. Yearb. Med. Inform. 28(01), 016–26 (2019)
Lundervold, A.S., Lundervold, A.: An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik. 29(2), 102–127 (2019)
Razzak, M.I., Naz, S., Zaib, A.: Deep learning for medical image processing: overview, challenges and the future. In: Classification in BioApps 2018, pp. 323–350. Springer, Cham
Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. IEEE Access 6, 9375–9389 (2017)
Hossain, M.S., Muhammad, G., Alamri, A.: Smart healthcare monitoring: a voice pathology detection paradigm for smart cities. Multimed. Syst. 25(5), 565–575 (2019)
Jia, B., Lv, J., Liu, D.: Deep learning-based automatic downbeat tracking: a brief review. Multimed. Syst. 25(6), 617–638 (2019)
Wang, Y., Zu, C., Ma, Z., Luo, Y., He, K., Wu, X., Zhou, J.: Patch-wise label propagation for MR brain segmentation based on multi-atlas images. Multimed. Syst. 25(2), 73–81 (2019)
Zhao, F., Chen, Y., Hou, Y., He, X.: Segmentation of blood vessels using rule-based and machine-learning-based methods: a review. Multimed. Syst. 25(2), 109–118 (2019)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bhatt, C., Kumar, I., Vijayakumar, V. et al. The state of the art of deep learning models in medical science and their challenges. Multimedia Systems 27, 599–613 (2021). https://doi.org/10.1007/s00530-020-00694-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00530-020-00694-1