A Review on Exploring the Deep Learning Concepts and Applications for Medical Diagnosis
MLA Style: P. Seetha Subha Priya, S. Nandhinidevi, Dr. M. Thangamani, Dr. S. Nallusamy "A Review on Exploring the Deep Learning Concepts and Applications for Medical Diagnosis" International Journal of Engineering Trends and Technology 68.10(2020):63-66.
APA Style:P. Seetha Subha Priya, S. Nandhinidevi, Dr. M. Thangamani, Dr. S. Nallusamy. A Review on Exploring the Deep Learning Concepts and Applications for Medical Diagnosis International Journal of Engineering Trends and Technology, 68(10),63-66.
Deep Learning (DL) benefits significance among researchers, from both academia and Industry. DL algorithms show the facility to learn and model very large-scale data sets. Deep learning techniques have gained wide acceptance in performing different task especially in bioinformatics, medical analysis and drug discovery. In the recent years, DL theory in the field of artificial intelligence, neural network structure, optimization and natural language processing has seen exponentially growth and attention. This paper explores the knowledge representation of various methods and their applications of DL for disease prediction in the medical field.
 O. Yeon Kwon, Min Ho Lee, Cuntai Guan, Fellow and Seong and Whan Lee, Subject independent brain computer interfaces based on deep convolutional neural networks, IEEE Transactions on Neural Networks and Learning systems, 31(10), 2020, 3839-3852.
 Kailun Wu, Yiwen Guo and Changshui Zhang, “Compressing deep neural networks with sparse matrix factorization”, IEEE Transactions on Neural Networks and Learning Systems, 31(10), 2020, 3828-3838.
 LF Borja-Borja et al., “A short review of deep learning methods for understanding group and crowd activities”, International Joint Conference on Neural Networks, 101-112, 2018.
 Dong, C., Loy, C.C., He, K and Tang, X, “Image super-resolution using deep convolutional networks”, IEEE Trans. Pattern Anal. Mach. Intel, 38(6), 2016, 295-307.
 Dou, Q. et al., “Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks”, IEEE Trans. Med. Imaging, 35(5), 2016, 1182-1195.
 Luo, Y., Cheng, Y., Uzuner, O., Szolovits, P and Starren, J, “Segment convolutional neural networks for classifying relations in clinical notes”, J. Am. Med. Inform. Assoc., 25(1), 2017, 93-98.
 Dey, D., Chaudhuri, S and Munshi, S, “Obstructive sleep apnoea detection using convolutional neural network based deep learning framework”, Biomed. Eng. Lett., 8(1), 2018, 95-100.
 Hu, Y., and Lu, X., “Learning spatial-temporal features for video copy detection by the combination of CNN and RNN”. J. Vis. Commun. Image Rep. 55, 2018, 21-29.
 Krizhevsky, A., Sutskever, I., and Hinton, G. E. “Imagenet classification with deep convolutional neural network”, Commun. ACM , 60, 2017, 84-90.
 Tulin Ozturk, Muhammed Talo, Eylul Azra Yildirim, Ulas Baran Baloglu, Ozal Yildirim, and U. Rajendra Acharya, “Automated detection of COVID-19 cases using deep neural networks with X-ray images”, Comput Biol Med., 18, 2020, 201-209.
 Valentina Bellemo et al., “Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: A clinical validation study”, Digital-Health, 1, 2019, 35-44.
 Ali Abbasian Ardakani et al., “Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks”, Comput Biol Med, 121, 2020, 542-555.
 Doctor HV, Nkhana-Salimu S, Abdulsalam and Anibilowo M, “Health facility delivery in sub-Saharan Africa: successes, challenges, and implications for the 2030 development agenda”, BMC Public Health, 18, 2018, 765-762.
 Alexandre Cunha Marquezine et al., “A case study through queue simulations of a basic health center”, International Journal of Engineering Trends and Technology, 68(6), 2020, 22-27.
 Hall V, Thomsen RW, Henriksen O and Lohse N, “Diabetes in sub saharan africa 1999-2011:Epidemiology and public health implications-A systematic review”, BMC Public Health, 11, 2011, 564-571.
 Sivaprasad S, Gupta B, Crosby-Nwaobi R and Evans J, “Prevalence of diabetic retinopathy in various ethnic groups: a worldwide perspective”, Surv Ophthalmol, 57, 2012, 347-370.
 Burgess PI, MacCormick IJ, Harding SP, Bastawrous A, Beare NA and Garner P, “Epidemiology of diabetic retinopathy and maculopathy in Africa: a systematic review”, Diabet Med , 30, 2014, 399-412.
 Daniel S. W. Ting , Paul H. Yi and Ferdinand Hui, “Clinical Applicability of Deep Learning System in Detecting Tuberculosis with Chest Radiography”, Journal of Radiology, 286(2), 2018, 11-21.
 Pranav Rajpurkar et al., “CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV”, Journal of NPJ Digital Medicine, 3, 2020, 112-124.
 Ryan Poplin et al, “Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning”, Nature Biomedical Engg., 2, 2018, 158-164.
 Tyler Hyungtaek Rim et al, “Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms”, Lancet Digital Health, 2, 2020, 526-536.
 Ting DSW, Pasquale LR and Peng L, “Artificial intelligence and deep learning in ophthalmology”. Br J Ophthalmol, 103, 2019, 167-175.
 Bustamante, Rodríguez, C., and Esenarro, D., “Real Time Facial Expression Recognition System Based on Deep Learning”, International Journal of Recent Technology and Engineering, 8, 2019, 4047-4051.
 Chang J, Ko A and Park SM, “Association of cardiovascular mortality and deep learning-funduscopic atherosclerosis score derived from retinal fundus images”, Am J Ophthalmol, 20, 2020, 231-245.
 Jie Xu, Kanmin Xue, and Kang Zhang, “Current status and future trends of clinical diagnoses via image-based deep learning”, Theranostics, 9(25), 2019, 7556-7565.
 LeCun Y, Bengio Y and Hinton G, Deep learning, Nature, 521, 2015, 436-444.
 Gulshan V. et al., “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”, JAMA, 316, 2016, 2402-2410.
 Middleton I and Damper RI, Segmentation of magnetic resonance images using a combination of neural networks and active contour models, Med Eng Phys, 26, 2004, 71-86.
 Pereira S, Pinto A, Alves V and Silva CA., “Brain tumor segmentation using convolutional neural networks in MRI images”. IEEE Trans Med Imaging, 35, 2016, 1240-1251.
 Ciresan DC, Giusti A, Gambardella and Schmidhuber J. Mitosis, “Detection in breast cancer histology images with deep neural networks”, Med Image Comput Comput Assist Interv., 16, 2013,411-418.
 Sobya, D. and Manoj, S, “Prediction and identification of cancer and normal genes through wavelet transform technique”, Indian Journal of Public Health Research and Development, 10(8), 2019, 631-637.
 Lee, et al., “Deep learning in medical imaging: general overview”, Korean J. Radiol, 18, 2017,570-584.
 Xiao M et al, “Diagnostic value of breast lesions between deep learning-based computer-aided diagnosis system and experienced radiologists: comparison the performance between symptomatic and asymptomatic patients”, Front Oncol., 10, 2020, 10-19
 Dongqi Han a, Kenji Doya b and Jun Tani, “The current paper proposes a novel multitimescale RNN architecture and an off-policy actor–critic algorithm for learning with multiple discount factors”, Neural Networks, 129, 2020, 149-162.
 YuLi, ChaoHuang, LizhongDing, ZhongxiaoLi, YijiePan and XinGao, “Deep learning in bioinformatics: Introduction, application, and perspective in the big data era”, Methods, 11, 2019, 4-21.
 Makkie, et al., “Fast and scalable distributed deep convolutional autoencoder for fMRI big data analytics”, Neurocomputing, 325, 2019, 20-30.
 Yue Cao, Thomas Andrew Geddes, Jean Yee Hwa Yang and Pengyi Yang, “Ensemble deep learning in bioinformatics”, BMC Bioinformatics, 2, 2020, 500-508.
 Eraslan, G., Avsec, Gagneur, J. and Theis, F. J, “Deep learning: New computational modelling techniques for genomic”, Nat. Rev. Genet. 20, 2019, 389-403.
 Li, Shi W and Wasserman, “Genome-wide prediction of cis-regulatory regions using supervised deep learning methods”, BMC Bioinformatics, 19, 2018, 22-29.
 Prasanna and Thangamani, “Cancer subtype discovery using prognosis-enhanced neural network classifier in metagenomic data, technology in cancer research & treatment”, Sage Publications, 17, 2018, 1-15.
 Bertil Schmidt and Andreas Hildebrandt, “Deep learning in next-generation sequencing”, 20, 2020, 1359-6446.
 Kushal K Dey, Bryce van de Geijn, Samuel Sungil Kim , Farhad Hormozdiari, David R Kelley and Alkes L Price, “Evaluating the informativeness of deep learning annotations for human complex diseases”, 11, 2020, 221-232.
 Zhenzhen Zou, Shuye Tian, Xin Gao, and Yu Li., “Multi-functional enzyme function prediction with hierarchical multi-label deep learning”, Frontiers in Genetics, 9, 2018, 1037-1048.
 S.K. Muruganandham, D. Sobya, S. Nallusamy, D K Mandal and Chakraborty, “Study on leaf segmentation using k-means and k-medoid clustering algorithm for identification of disease”, Indian J. of Public Health Research and Development, 9(2), 289-293, 2018.
 Hafiz A.M and Bhat G.M., “A survey of deep learning techniques for medical diagnosis information and communication technology for sustainable development”, Advances in Intelligent Systems and Computing, 933, 2020, 1030-147.
 Yue, L., Tian, D., Chen and W, “Deep learning for heterogeneous medical data analysis”, World Wide Web, 23, 2020, 2715–2737.
 Raji, C and Chandra, S.V., “Long-term forecasting the survival in liver transplantation using multilayer perceptron networks”, IEEE Transactions on Systems, Man, and Cybernetics: Systems. 47, 2017, 2318-2329.
 Jafar Ali Ibrahim and Dr. M. Thangamani, “Prediction of novel drugs and diseases for hepatocellular carcinoma based on multi-source simulated annealing based randomwalk”, Journal of Medical System, 42, 2018, 114-119.
 Shickel, B. et al., “A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis,” IEEE journal of biomedical and health informatics, 22, 2017, 1589-1604.
 Benjamin Shickel, Patrick James Tighe, Azra Bihorac and Parisa Rashidi, “Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis”, IEEE J Biomed Health Inform,22, 2018,1589-1604.
 Wolfgang Kopp, Remo Monti, Annalaura Tamburrini, Uwe Ohler and Altuna Akalin, Deep learning for genomics using Janggu, Nature Communications , 11, 2020, 212-221.
 Zou Z, Tian S, Gao X and Li Y, “Multi-functional enzyme function prediction with hierarchical multi-label deep learning”, Front. Genet. 9, 2019, 2223-2245.
Artificial Neural Network, Convolution Neural Network, Deep Learning, Medical Diagnosis, Machine Learning, Recurrent Neural Network