Vol. 4 No. 1 (2024): Journal of Deep Learning in Genomic Data Analysis
Articles

Deep Learning-Based Medical Image Enhancement for Improved Visualization

Rajeev Ranjan
Asst. Professor, Jodhpur Institute of Engineering and Technology, Jodhpur, India
Cover

Published 16-04-2024

Keywords

  • Deep learning,
  • Medical image enhancement,
  • Visualization,
  • Diagnostic accuracy

How to Cite

[1]
Rajeev Ranjan, “Deep Learning-Based Medical Image Enhancement for Improved Visualization”, Journal of Deep Learning in Genomic Data Analysis, vol. 4, no. 1, pp. 17–25, Apr. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://thelifescience.org/index.php/jdlgda/article/view/4

Abstract

Medical imaging plays a crucial role in modern healthcare for the diagnosis and treatment of various diseases. However, the quality of medical images can sometimes be suboptimal due to factors such as noise, low resolution, and artifacts. In this paper, we propose a deep learning-based approach for enhancing medical images to improve visualization and diagnostic accuracy. We demonstrate the effectiveness of our approach on a dataset of medical images, showing significant improvements in image quality and diagnostic performance. Our findings suggest that deep learning-based image enhancement techniques have the potential to revolutionize medical imaging and improve patient care.

Downloads

Download data is not yet available.

References

  1. i Pillai, Aravind Sasidharan. "Advancements in Natural Language Processing for Automotive Virtual Assistants Enhancing User Experience and Safety." Journal of Computational Intelligence and Robotics 3.1 (2023): 27-36.
  2. ii Venigandla, Kamala. "Integrating RPA with AI and ML for Enhanced Diagnostic Accuracy in Healthcare." Power System Technology 46.4 (2022).
  3. iii Nalluri, Mounika, et al. "INTEGRATION OF AI, ML, AND IOT IN HEALTHCARE DATA FUSION: INTEGRATING DATA FROM VARIOUS SOURCES, INCLUDING IOT DEVICES AND ELECTRONIC HEALTH RECORDS, PROVIDES A MORE COMPREHENSIVE VIEW OF PATIENT HEALTH." Pakistan Heart Journal 57.1 (2024): 34-42.
  4. iv Shiwlani, Ashish, et al. "REVOLUTIONIZING HEALTHCARE: THE IMPACT OF ARTIFICIAL INTELLIGENCE ON PATIENT CARE, DIAGNOSIS, AND TREATMENT." JURIHUM: Jurnal Inovasi dan Humaniora 1.5 (2024): 779-790.
  5. v Kumar, Mungara Kiran, et al. "Approach Advancing Stock Market Forecasting with Joint RMSE Loss LSTM-CNN Model." Fluctuation and Noise Letters (2023).
  6. vi Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.