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What are the most effective methods for lung cancer detection?

Support vector machine

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The most effective methods for lung cancer detection include image processing and machine learning techniques . Classification algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and decision tree (DT) methods have been used to classify lung images as malignant or normal based on extracted features . Additionally, deep learning models, such as convolutional neural networks (CNN), have been employed for tumor identification on CT scan images . Hybrid methods combining different features, such as Gray-level co-occurrence (GLCM), autoencoder, and Haralick texture features, have shown high detection performance using SVM with different kernel functions . Biosensor technology has also been utilized for the detection of lung cancer tumor markers, including DNA biosensors, RNA biosensors, and protein biosensors . These methods provide important insights for the early diagnosis of lung cancer .

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Journal Article•DOI Recent progress of biosensors for the detection of lung cancer markers. Shanchuan Chen, Minghan Li, Ting Weng, Deqiang Wang - Show less +3 more 02 Jun 2023-Journal of Materials Chemistry B	The paper discusses various methods for detecting lung cancer tumor markers, including electrochemical methods such as EIS and FET, optical sensors like SERS and SPR, and novel sensing platforms such as biological nanopore and solid-state nanopore sensors.
Proceedings Article•DOI Lung Cancer Detection using Transfer Learning and EfficientNet B2 Architecture 11 May 2023	The paper proposes a deep learning-based strategy for lung cancer detection using transfer learning and the EfficientNet B2 architecture.
Open access•Posted Content•DOI Improving Lungs Cancer Detection Based on Hybrid Features and Employing Machine Learning Techniques 24 May 2023 1 Citations	The most effective methods for lung cancer detection in the provided paper are the hybrid methods of GLCM + Autoencoder and Haralick + Autoencoder, using SVM Gaussian and radial base function (RBF) with 100% accuracy and AUC 1.00.
Open access•Journal Article•DOI Classification techniques using gray level co-occurrence matrix features for the detection of lung cancer using computed tomography imaging 01 Oct 2023-International Journal of Power Electronics and Drive Systems	The most effective method for lung cancer detection in the provided paper is the support vector machine (SVM) with an accuracy of 99.32%.

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