Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62328
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Information Technology
Chaitanya Bharathi Institute of Technology
Information Technology
Chaitanya Bharathi Institute of Technology
Information Technology
Chaitanya Bharathi Institute of Technology
Information Technology
Chaitanya Bharathi Institute of Technology
Information Technology
Chaitanya Bharathi Institute of Technology
The advancements in deep learning technologies have transformed disease diagnosis and treatment processes through increased speed, accuracy, and personalized decision-making. In contrast with other approaches, deep learning-based models are able to utilize their capability in analyzing complex medical data and recognizing hidden patterns that may go undetected by both humans and existing approaches. Deep learning methods have demonstrated substantial impact on cancer, neurological, and cardiac diseases diagnosis, outcome prediction, and customization of medical treatments. Based on studies and examples from clinical settings, it is evident that deep learning methods are capable of equaling and even outperforming the work performed by medical professionals in particular areas. An effective approach that has been developed as part of deep learning research is Generative Adversarial Network (GAN) that allows generating realistic synthetic medical data. In case of rare diseases and anomalies, GAN could provide sufficient data for training models, making them aware of the features of particular diseases and conditions. While there are certain challenges that should be addressed in deep learning, including patients’ privacy, collecting of high-quality data, and making the models interpretable by medical specialists, future advancements in this area will involve multi-modal data use, as well as development of explainable AI.
KEYWORDS
In this research paper, an advanced system based on deep learning principles has been developed for improving the efficiency of early detection systems. In this context, by combining convolutional neural networks with optimal training strategies, the proposed model has shown considerable per-formance improvements in terms of accuracy and precision. Deep learning can be a breakthrough innovation in the field of diagnosing diseases, yet it is highly dependent on the availability of large-scale datasets, especially in cases where the disease is relatively rare. However, this problem can easily be addressed by generating synthetic data using GANs. The findings of this research prove that the proposed solu-tion can effectively overcome the constraints of conventional approaches.
[1] D. Saxena, M. H. Siddiqui, and R. Kumar, “Deep Learning in Disease Diagnosis: Models and Datasets,” Current Bioinformatics, vol. 16, no. 5, pp. 632–643, Aug. 2021, doi: 10.2174/1574893615999201002124021. [2] L. Jovanovic, A. Petrovic, T. Zivkovic, M. Antonijevic, N. Bacanin, and M. Zivkovic, “Exploring the Potential of Generative Adversar-ial Networks for Synthetic Medical Data Generation,” in Proc. 31st Telecommunications Forum (TELFOR), IEEE, Nov. 2023, pp. 1–4, doi: 10.1109/telfor59449.2023.10372727. [3] “Synthetic data generation using GANs,” arXiv:2304.03243, 2023, doi: 10.48550/arXiv.2304.03243. [4] A. K. Conduah, S. Ofoe, and D. Siaw-Marfo, “Data privacy in health-care: Global challenges and solutions,” Digital Health, vol. 11, May 2025, doi: 10.1177/20552076251343959. [5] J. Lee et al., “Deep Learning for Rare Disease: A Scoping Review,” openRxiv, Jun. 2022, doi: 10.1101/2022.06.29.22277046. [6] S. Decherchi, E. Pedrini, M. Mordenti, A. Cavalli, and L. San-giorgi, “Opportunities and Challenges for Machine Learning in Rare Diseases,” Frontiers in Medicine, vol. 8, Oct. 2021, doi: 10.3389/fmed.2021.747612. [7] M. K. Goyal, “Synthetic Data Revolutionizes Rare Disease Research,” Int. J. Recent Innov. Trends Comput. Commun., vol. 11, no. 11, pp. 1368–1380, Dec. 2023, doi: 10.17762/ijritcc.v11i11.11411. [8] A. Arora and A. Arora, “Generative adversarial networks and synthetic patient data,” Future Healthcare Journal, vol. 9, no. 2, pp. 190–193, Jul. 2022, doi: 10.7861/fhj.2022-0013. [9] C. Yan, Z. Zhang, S. Nyemba, and Z. Li, “Generating Synthetic Electronic Health Record Data Using GANs,” JMIR AI, vol. 3, p. e52615, Apr. 2024, doi: 10.2196/52615.
Publication Date
5/4/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
9
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...