ItemBook Chapter

A comparative evaluation of machine learning and deep learning models across diverse datasets for early detection of lung cancer

Title
A comparative evaluation of machine learning and deep learning models across diverse datasets for early detection of lung cancer
Creator
Angeline, A.; Margaret Savitha, P.; Sahayam, Norra
Description
Lung cancer is among the most fatal types of cancer, accounting for millions of fatalities globally. The capacity for its early detection has the potential to greatly enhance the outcome of treatments, and in recent times, machine learning (ML) and deep learning (DL) algorithms have emerged as mighty resources in aiding radiologists and doctors. This article describes a comparison study of research articles in which they employed different ML and DL models in lung cancer detection on a wide range of datasets. The analysis establishes that the variety, quality, and source of the dataset are central to determining how reliable reported model performance is. Reproducibility has been made possible with public datasets such as LIDC-IDRI, NSCLC, and Kaggle datasets, whereas private clinical datasets typically lead to improved accuracy since they consist of high-quality curated annotations. Subsequent research has shown that DL models, especially state-of-the-art architectures such as convolutional neural networks (CNNs) and EfficientNet-B3, are well-suited to image classification tasks and consistently outperform classical ML models when large, well-balanced datasets are available. Hybrid approaches that blend CNN-based feature learning with traditional classifiers like support vector machines have also proven highly promising, particularly when applied to overcome challenges such as small sample sizes and noisy images. Directions for future work point toward the integration of standardized, multicenter datasets, explainable AI models, and multimodal learning techniques to reach reliable in-clinic deployment. 2026 Elsevier Inc. All rights reserved.
Source
Explainable AI in Clinical Practice: Methods, Applications, and Implementation;pp.45-67
Date
01-01-2026
Publisher
Elsevier
Subject
artificial intelligence; comparative analysis; deep learning; early detection; ensemble learning; lung cancer; Machine learning
Coverage
Angeline A., Christ University, Karnataka, Bangalore, India; Margaret Savitha P., Christ University, Karnataka, Bangalore, India; Sahayam N., Data Solution Architect DXC Technologies, United Kingdom
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 978-044344111-0; 978-044344112-7;
Format
online
Language
English
Type
Book chapter

Citation

Angeline, A.; Margaret Savitha, P.; Sahayam, Norra, “A comparative evaluation of machine learning and deep learning models across diverse datasets for early detection of lung cancer,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/24249.

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