Lung Cancer Detection and Classification Using Machine Learning: A Literature Review
DOI:
https://doi.org/10.54536/ajiri.v4i1.3797Keywords:
Detection and Classification, Lung Lesion, Machine Learning, Preprocessing, SensitivityAbstract
Lung cancer is one of the pressing public health issues needing accurate and timely diagnosis. Machine learning (ML) is an effective method for analyzing medical images and supporting lung cancer diagnosis and it has significant potential to advance medical practice. This review explored the efficacy of current machine learning methods in detecting and classifying lung cancer. It analyzes the studies on preprocessing techniques, detection accuracy, and classification performance. Preprocessing techniques have significantly improved image quality through noise cancellation and feature enhancement, making it highly efficient. The sensitivity of the machine learning algorithms used for identification of lung cancer is also high, surpassing 90% of some research. This translates to a high probability of correctly identifying actual cancer cases. Support Vector Machines (SVM), Random Forest, and Convolutional Neural Networks (CNN) are among the most effective algorithms. Furthermore, machine learning accurately classifies lung nodules as benign or malignant, exceeding 85% in reported studies. SVM and K-Nearest Neighbor (KNN) are commonly used classification methods with promising results. Through continued research efforts to overcome existing challenges, machine learning could achieve heightened accuracy, seamless integration into clinical practice, and improved outcomes for patients with lung cancer.
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Copyright (c) 2025 Lenard Abiel D. Aure, Janice Angela V. Paco, Jessica Z. Panganiban, Cereneo S. Santiago Jr, Gersom S. Baradi
This work is licensed under a Creative Commons Attribution 4.0 International License.
