Vol. 3 No. 1 (2023): Journal of Deep Learning in Genomic Data Analysis
Articles

Deep Learning-Based Pathology Image Analysis for Cancer Diagnosis

Dr. Mei Chen
Associate Professor, AI in Public Health, Lotus University, Shanghai, China
Cover

Published 16-04-2023

Keywords

  • Deep learning,
  • pathology image analysis,
  • cancer diagnosis,
  • convolutional neural networks

How to Cite

[1]
Dr. Mei Chen, “Deep Learning-Based Pathology Image Analysis for Cancer Diagnosis”, Journal of Deep Learning in Genomic Data Analysis, vol. 3, no. 1, pp. 1–7, Apr. 2023, Accessed: May 18, 2024. [Online]. Available: http://thelifescience.org/index.php/jdlgda/article/view/8

Abstract

This research paper explores the application of deep learning algorithms in the field of pathology image analysis for the purpose of cancer diagnosis. Pathology images are crucial for diagnosing various diseases, including cancer, but their analysis can be time-consuming and prone to human error. Deep learning models have shown promising results in automating this process, potentially improving diagnostic accuracy and efficiency. This study evaluates the performance of different deep learning algorithms on pathology images to assess their suitability for aiding in the diagnosis of cancer and other diseases. Through this analysis, we aim to contribute to the growing body of literature on the use of deep learning in healthcare and its potential impact on improving diagnostic processes.

Downloads

Download data is not yet available.

References

  1. Veronin, Michael A., et al. "Opioids and frequency counts in the US Food and Drug Administration Adverse Event Reporting System (FAERS) database: A quantitative view of the epidemic." Drug, Healthcare and Patient Safety (2019): 65-70.
  2. Reddy, Byrapu, and Surendranadha Reddy. "Evaluating The Data Analytics For Finance And Insurance Sectors For Industry 4.0." Tuijin Jishu/Journal of Propulsion Technology 44.4 (2023): 3871-3877.
  3. Dixit, Rohit R. "Investigating Healthcare Centers' Willingness to Adopt Electronic Health Records: A Machine Learning Perspective." Eigenpub Review of Science and Technology 1.1 (2017): 1-15.
  4. Pillai, Aravind Sasidharan. "Multi-label chest X-ray classification via deep learning." arXiv preprint arXiv:2211.14929 (2022).
  5. Venigandla, Kamala. "Integrating RPA with AI and ML for Enhanced Diagnostic Accuracy in Healthcare." Power System Technology 46.4 (2022).
  6. Khan, Mohammad Shahbaz, et al. "Improving Multi-Organ Cancer Diagnosis through a Machine Learning Ensemble Approach." 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2023.
  7. Kumar, Bonda Kiran, et al. "Predictive Classification of Covid-19: Assessing the Impact of Digital Technologies." 2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2023.
  8. Vemuri, Navya, and Kamala Venigandla. "Autonomous DevOps: Integrating RPA, AI, and ML for Self-Optimizing Development Pipelines." Asian Journal of Multidisciplinary Research & Review 3.2 (2022): 214-231.