Lung Cancer Detection Using Linear Discriminant Analysis for Feature Extraction and Adaboost Classification
Keywords:
Lung cancer detection, Linear Discriminant Analysis, AdaBoost algorithm, feature extraction, AI in healthcareAbstract
Early, accurate diagnosis of lung cancer determines survival rates. Lung cancer is still one of the most often occurring and fatal forms of cancer known to exist worldwide. Sometimes conventions in diagnosis present challenges including high costs, results that vary for every patient, and treatments needing a lot of time. Using artificial intelligence (AI) based technologies offers one fascinating way to address these issues. This work suggests a fresh method of lung cancer detection. For classification study, the method uses AdaBoost and Linear Discriminant Analysis (LDA) to extract features. Preprocessing of lung cancer image datasets forms the first phase of the proposed method. One can reach this level by first removing noise and improving image quality. LDA guarantees efficient processing while preserving required information by means of the extraction of the most unique characteristics, so reducing dimensionality. After that, the AdaBoost method aggregates several weak classifiers into a strong model to increase the accuracy of classification using these traits. Comparing the results with an accuracy above 95% showed that the LDA-AdaBoost model outperformed more traditional techniques
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