Abstract
Deep learning (DL) is a machine learning optimization technique that has represented amazing performance in identifying obscure structures in high-dimensional data to find the most optimal settings. Thus, DL has been effectively applied in many diverse fields in image and speech recognition, visual art, natural language processing, and bioinformatics. Other than this, lots more are still needed to be investigated. This paper systematically reviews publications used in deep learning methods for the prediction of chronic diseases more accurately. In medical science, it is always challenging to analyze chronic diseases before the major damages. Some of the chronic diseases cannot be recognized in primary diagnosis until they put a drastic impact on health, as some of them have no treatment. Hence, to avoid such an awful condition, there is a sturdy requirement of some models that can predict disease more accurately in an early stage. Different models have been designed using deep learning’s multilayer approach and provide better result in the prediction of some chronic diseases that comprises Coronary Heart disease, Alzheimer disease, labeling of multiple chronic disease, Diabetic Retinopathy, Breast cancer, Autoimmune disease, and skin diseases. This paper summarizes all of these DL models for predicting the mentioned diseases.
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References
Akselrod-Ballin A, Karlinsky L, Alpert S, Hasoul S, Ben-Ari R, Barkan E (2016) A region based convolutional network for tumor detection and classification in breast mammography. In: Deep learning and data labeling for medical applications. Springer, Cham, pp 197–205
Bottou L (2012) Stochastic gradient descent tricks. In: Neural networks: Tricks of the trade. Springer, Berlin, Heidelberg, pp 421–436
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115
Gargeya R, Leng T (2017) Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7):962–969
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Kim R (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama 316(22):2402–2410
Han XH, Lei J, Chen YW (2016) HEp-2 cell classification using K-support spatial pooling in deep CNNs. In: Deep learning and data labeling for medical applications. Springer, Cham, pp 3–11
Hosseini-Asl E, Keynton R, El-Baz A (2016) Alzheimer's disease diagnostics by adaptation of 3D convolutional network. In: 2016 IEEE international conference on image processing (ICIP). IEEE, pp 126–130
Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng D (2014) Early diagnosis of Alzheimer's disease with deep learning. In: 2014 IEEE 11th international symposium on biomedical imaging (ISBI). IEEE, pp 1015–1018
Mason L, Baxter J, Bartlett PL, Frean MR (2000) Boosting algorithms as gradient descent. In: Advances in neural information processing systems, pp 512–518
Mazurowski MA, Buda M, Saha A, Bashir MR (2018) Deep learning in radiology: an overview of the concepts and a survey of the state of the art. arXiv preprint arXiv:1802.08717
Ng A (2011) Sparse autoencoder. CS294A Lecture notes 72(2011):1–19
Ngiam J, Chen Z, Bhaskar SA, Koh PW, Ng AY (2011) Sparse filtering. In: Advances in neural information processing systems, pp 1125–1133
Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, Peng L, Webster DR (2018) Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2(3):158
Razavi F, Tarokh MJ, Alborzi M (2019) An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning. J Big Data 6.1, 32
Swain PH, Hauska H (1977) The decision tree classifier: design and potential. IEEE Trans Geosci Electron 15(3):142–147
Wang J, Ding H, Bidgoli FA, Zhou B, Iribarren C, Molloi S, Baldi P (2017) Detecting cardiovascular disease from mammograms with deep learning. IEEE Trans Med Imaging 36(5):1172–1181
Wang Z, Shang X (2006) Spatial pooling strategies for perceptual image quality assessment. In: 2006 international conference on image processing. IEEE, pp 2945–2948
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Sahni, G., Lalwani, S. (2021). Deep Learning Methods for the Prediction of Chronic Diseases: A Systematic Review. In: Mathur, R., Gupta, C.P., Katewa, V., Jat, D.S., Yadav, N. (eds) Emerging Trends in Data Driven Computing and Communications. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-3915-9_8
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DOI: https://doi.org/10.1007/978-981-16-3915-9_8
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