Machine Learning Approaches to Lung Cancer Prediction: A Comparative Study
- DOI
- 10.2991/978-94-6463-787-8_3How to use a DOI?
- Keywords
- Lungs cancer; deep learning; convolutional neural network; artificial neural network; recurrent neural networks
- Abstract
Lung cancer continues to rank among the most common and fatal types of cancer worldwide, significantly affecting both patient quality of life and public health. Improving treatment results and survival rates requires early detection and precise diagnosis. In recent years, deep learning algorithms have shown promise in increasing the accuracy of lung cancer prediction. Large datasets are needed for deep learning (DL) in order to train the model. However, the sample size of the current dataset is small, which limits the model’s generalizability. We conduct a comparative analysis for lung cancer forecasts in this research. The model was trained using DL algorithms such as recurrent neural networks (RNN), convolutional neural networks (CNN), and artificial neural networks (ANN), SVM (Support vector machine) and VGG-16.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Sunita Ranadhir Landge AU - Dinesh Jain PY - 2025 DA - 2025/07/17 TI - Machine Learning Approaches to Lung Cancer Prediction: A Comparative Study BT - Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025) PB - Atlantis Press SP - 14 EP - 23 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-787-8_3 DO - 10.2991/978-94-6463-787-8_3 ID - Landge2025 ER -