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: Nov. 21, 2024. [Online]. Available: https://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.

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