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- W4313888538 abstract "Lung cancer is by far the primary cause of cancer deaths globally. Computer-aided diagnosis (CAD) system is used for the prediction of lung cancer which helps to attain a high detection rate and reduces the time consumed for analyzing the sample. In this paper, CAD system based on sputum color images is proposed which consists of four main processing steps. It starts with the preprocessing step using a heuristic rule-based and a Bayesian classification method using the histogram analysis. In this step, the region of interest (ROI) representing the sputum cell is detected and extracted. In order to segment the nuclei from the cytoplasm, mean shift segmentation is used. The next step is feature analysis. Finally, the diagnosis is done using a rule-based algorithm alongside the artificial neural network (ANN) and support vector machine (SVM) for identifying cancerous and non-cancerous cells. The performance evaluation was done based on the sensitivity, specificity, and accuracy. Our methods are validating by using a set of experiments conducted with a data set of 100 images. The final results showed that the techniques used outperformed conventional methods. The proposed CAD system achieved a reasonable accuracy above 95% with high true positive rates that can basically meet the requirement of clinical diagnosis." @default.
- W4313888538 created "2023-01-10" @default.
- W4313888538 creator A5015357458 @default.
- W4313888538 date "2023-01-01" @default.
- W4313888538 modified "2023-09-25" @default.
- W4313888538 title "Early Lung Cancer Detection by Using Artificial Intelligence System" @default.
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- W4313888538 doi "https://doi.org/10.1007/978-3-031-15816-2_19" @default.
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