Performance Analysis of Various Machine Learning and Deep Learning Approaches for the Detection of Lung Cancer - read online free

Aug 5, 2026

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Lung cancer remains one of the deadliest cancers globally, making early detection and accurate classification critical for survival. Machine learning (ML) and deep learning (DL) have advanced medical diagnostics by enabling highly accurate analysis of complex imaging data such as CT scans. This study reviews ML/DL methods for detecting and classifying lung cancer and highlights models—including PCA, KNearest Neighbors, SVM, Naïve Bayes, Decision Trees, Artificial Neural Networks, and deep learning—to identify nodules and distinguish benign from malignant tumors while reducing false positives and supporting timely clinical intervention.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

Data mining and machine learning fundamentals

ML learning paradigms

Review of the Literature

Deep learning, neural networks, and model advances

Lung cancer screening with 3D deep learning

Performance Analysis of Various Machine Learning and Deep Learning Approaches for the Detection of Lung Cancer SEEJPH Volume XXIV S4, 2024, ISSN: 2197-5248; Posted: 25-10-2024 Performance Analysis of Various Machine Learning and Deep Learning Approaches for the Detection of Lung Cancer K. Ramkumar1, M. Natarajan2 1Research Scholar, Department of Computer and Information Science, Faculty of Science, Annamalai University, Annamalainagar, Tamil Nadu, India. 2Department of Computer and Information Science, Faculty of Science, Annamalai University, Annamalainagar, Tamil Nadu, India. [Email:](Email:1ramau13@gmail.com)[1](Email:1ramau13@gmail.com)[ramau13@gmail.com](Email:1ramau13@gmail.com), [2](2mind.2004@gmail.com)[mind.2004@gmail.com](2mind.2004@gmail.com) | KEYWORDS
Lung Cancer; Classification; Machine Learning; Artificial Neural
Networks; Support Vector Machines; Decision Tree; Naïve Bayes; Deep Learning. | ABSTRACT:
Lung cancer is one of the deadliest forms of cancer worldwide, making early detection essential for improving survival outcomes. The use of machine learning (ML) and deep learning (DL) has brought significant advancements to medical diagnostics, providing exceptional accuracy in detecting lung cancer. This text examines the use of ML and DL methods for the detection and classification of lung cancer, emphasizing their effectiveness in analyzing complex medical imaging data, such as CT scans. Sophisticated models, like Principal Component Analysis, KNearest Neighbors, Support Vector Machines, Naïve Bayes, Decision Trees, Artificial Neural Networks, and Deep learning techniques, have shown outstanding performance in identifying lung nodules and differentiating between benign and malignant tumors. These approaches not only improve diagnostic accuracy but also minimize false positives, enabling timely and appropriate medical intervention. | | --- | --- | 1. Introduction Lung cancer continues to be one of the leading causes of cancer-related deaths globally, underscoring the critical importance of early detection and precise classification. Advances in computational methods have made machine learning (ML) and deep learning (DL) approaches increasingly vital for enhancing diagnostic accuracy. This article offers an in-depth performance analysis of various ML and DL techniques applied to lung cancer detection and classification. By comparing these methods, the study seeks to identify the most effective solutions, shedding light on their advantages, limitations, and potential clinical applications. The evaluation delivers valuable insights into the rapidly advancing field of AI-driven cancer diagnostics. Data mining is a key process within data science focused on extracting valuable patterns and insights from large datasets. Integrating techniques from statistics, machine learning, database systems, and artificial intelligence, it reveals hidden patterns, correlations, and trends that are not immediately obvious. This process is essential for transforming raw data into actionable information, supporting decision-making, prediction, and strategic planning across various Performance Analysis of Various Machine Learning and Deep Learning Approaches for the Detection of Lung Cancer SEEJPH Volume XXIV S4, 2024, ISSN: 2197-5248; Posted: 25-10-2024 fields such as healthcare, finance, and marketing. It involves multiple stages, including data preprocessing, model building, pattern discovery, and interpretation, making it indispensable in today's data-centric environment. According to Han, Pei, and Kamber (2011), data mining has grown significantly in importance due to the exponential increase in data generated across industries. The ability to analyze and interpret large volumes of data is essential for organizations seeking to remain competitive ina rapidly evolving market landscape. Machine learning, a branch of artificial intelligence (AI), centers on creating algorithms and statistical

FAQs

Why is early detection of lung cancer emphasized in the study?

Early detection is essential because lung cancer is one of the leading causes of cancer-related deaths, and accurate classification improves survival outcomes and clinical decision-making.

What imaging data and tasks do the ML/DL approaches target?

The approaches focus on analyzing complex medical imaging data such as CT scans, aiming to detect lung nodules and classify tumors as benign or malignant.

Which models are highlighted as effective for lung cancer detection and classification?

The text emphasizes PCA, K-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Decision Trees, Artificial Neural Networks, and deep learning techniques for strong performance and reduced false positives/negatives.

Aug 5, 2026

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