Accessibility settings

This paper is in the following e-collection/theme issue:

Theme Issue 2025: Artificial Intelligence in Oncology (12) Clinical Informatics (1483) Imaging Informatics (350) Machine Learning (2298) Lung Cancer (76) Medical imaging (143)

Published on in Vol 6 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68848, first published .
Lung Cancer Diagnosis From Computed Tomography Images Using Deep Learning Algorithms With Random Pixel Swap Data Augmentation: Algorithm Development and Validation Study

Lung Cancer Diagnosis From Computed Tomography Images Using Deep Learning Algorithms With Random Pixel Swap Data Augmentation: Algorithm Development and Validation Study

Lung Cancer Diagnosis From Computed Tomography Images Using Deep Learning Algorithms With Random Pixel Swap Data Augmentation: Algorithm Development and Validation Study

Authors of this article:

Ayomide Adeyemi Abe1 Author Orcid Image ;   Mpumelelo Nyathi1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Khan S, Noor M, Ashraf I, Masud M, Aman M. Impact of CT Intensity and Contrast Variability on Deep-Learning-Based Lung-Nodule Detection: A Systematic Review of Preprocessing and Harmonization Strategies (2020–2025). Diagnostics 2026;16(2):201 View
  2. Khan S, Noor M, Alshahrani H, Bouchelligua W, Ashraf I. Improving Deep Learning Based Lung Nodule Classification Through Optimized Adaptive Intensity Correction. Bioengineering 2026;13(4):396 View

Conference Proceedings

  1. Kumar G, Maram B. 2026 9th International Conference on Intelligent Computing and Control Systems (ICICCS). An Interpretable Hybrid Intelligence Framework Integrating Demographic Profiling and High-Resolution CT Imaging for Early-Stage Lung Cancer Multiclass Classification View