Deep Learning Approaches for Medical Image Processing in the Big Data Era
DOI:
https://doi.org/10.58599/IJSMCSE.2024.1108Keywords:
Medical image processing, Deep learning, Big data, Transfer learning, Federated learning, Multi-modal imagingAbstract
Diagnostics, therapy planning, and patient monitoring are areas where medical image processing has become indispensable in modern healthcare. The advent of deep learning techniques and the broad usage of big data in healthcare have brought a revolutionary shift in medical picture analysis and interpretation. An overview of deep learning techniques for medical image processing based on big data is given in this article. This article covers the pros and cons of adopting big data for medical imaging, from data storage and analysis to data capture. Beyond that, we take a look at medical image analysis using deep learning algorithms such as recurrent neural network (RNN), Convolutional Neural Network (CNN), and generative adversarial network (GAN), and we highlight its advantages and disadvantages. We also examine recent innovations such as transfer learning, multi-modal imaging fusion, and federated learning, which can improve the accuracy and efficiency of medical image processing systems. Finally, we discuss how medical image processing driven by deep learning could improve clinical decision-making, patient outcomes, and the development of personalized medicine in the era of data-driven healthcare.
References
[1]. Christian Tchito Tchapga, Thomas Attia Mih, Aurelle Tchagna Kouanou, Theophile Fozin Fonzin, Platini Kuetche Fogang, Brice Anicet Mezatio, and Daniel Tchiotsop. Biomedical image classification in a big data architecture using machine learning algorithms. Journal of Healthcare Engineering, 2021:1-11, 2021.
[2]. Venkata Krishna Chaithanya Manam. Efficient disambiguation of task instructions in crowdsourcing. PhD thesis, Purdue University Graduate School, 2023.
[3]. Daphne Lopez and MA Durai. HCI Challenges and Privacy Preservation in Big Data Security. IGI Global, 2017.
[4]. Wullianallur Raghupathi and Viju Raghupathi. Big data analytics in healthcare: promise and potential. Health information science and systems, 2:1–10, 2014.
[5]. Yichuan Wang, LeeAnn Kung, and Terry Anthony Byrd. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological forecasting and social change, 126:3–13, 2018.
[6]. VK Chaithanya Manam, Joseph Divyan Thomas, and Alexander J Quinn. Tasklint: Automated detection of ambiguities in task instructions. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, volume 10, pages 160–172, 2022.
[7]. Sanjeev Kulkarni, Aishwarya Shetty, Mimitha Shetty, HS Archana, and B Swathi. Gas spilling recognition and prevention using iot with alert system to improve the quality service. Perspectives in Communication, Embedded-systems and Signal-processing-PiCES, 4(4):34–38, 2020.
[8]. Ashwin Belle, Raghuram Thiagarajan, SM Soroushmehr, Fatemeh Navidi, Daniel A Beard, Kayvan Najarian, et al. Big data analytics in healthcare. BioMed research international, 2015, 2015.
[9]. V Suresh Kumar, Sanjeev Kulkarni, Naveen Mukkapati, Abhinav Singhal, Mohit Tiwari, and D Stalin David. Investigation on constraints and recommended context aware elicitation for iot runtime workflow. International Journal of Intelligent Systems and Applications in Engineering, 12(3s):96–105, 2024.
[10]. V K. Chaithanya Manam, Dwarakanath Jampani, Mariam Zaim, Meng-Han Wu, and Alexander J. Quinn. Taskmate: A mechanism to improve the quality of instructions in crowdsourcing. In Companion Proceedings of The 2019 World Wide Web Conference, pages 1121–1130, 2019.
[11]. Aurelle Tchagna Kouanou, Daniel Tchiotsop, Romanic Kengne, Djoufack Tansaa Zephirin, Ngo Mouelas Adele Armele, and Ren´e Tchinda. An optimal big data workflow for biomedical image analysis. Informatics in Medicine Unlocked, 11:68–74, 2018.
[12]. Sanjeev Kulkarni, Sachidanand S Joshi, AM Sankpal, and RR Mudholkar. Link stability based multipath video transmission over manet. International Journal of Distributed and Parallel Systems, 3(2):133, 2012.
[13]. Ahmed Oussous, Fatima-Zahra Benjelloun, Ayoub Ait Lahcen, and Samir Belfkih. Big data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 30(4):431–448, 2018.
[14]. Rehan Ashraf, Muhammad Asif Habib, Muhammad Akram, Muhammad Ahsan Latif, Muhammad Sheraz Arshad Malik, Muhammad Awais, Saadat Hanif Dar, Toqeer Mahmood, Muhammad Yasir, and Zahoor Abbas. Deep convolution neural network for big data medical image classification. IEEE Access, 8:105659–105670, 2020.
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Methods in Computational Science and Engineering
![Creative Commons License](http://i.creativecommons.org/l/by-nc-nd/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.