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- W4387251018 abstract "Lung cancer is the most fatal type of cancer, which results from abnormal cell growth in the lung tissue. It is a leading cause of cancer-related deaths globally, and early detection is crucial for successful treatment and improved patient outcomes. Various methods are available for detecting lung cancer, such as imaging techniques like chest X-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI), as well as non-invasive diagnostic methods like liquid biopsy. Liquid biopsy involves identifying biomarkers in blood or other bodily fluids, such as circulating tumor cells (CTCs) or cell-free DNA (cfDNA), which can indicate the presence of lung cancer. The proposed system uses CT scans to obtain lung tissue images and applies image processing algorithms to identify and classify potentially cancerous regions. In addition, the system uses liquid biopsy to detect CTCs or cfDNA in the patient's blood. The system employs a multi-stage process, including pre-processing, segmentation, feature extraction, and classification, to enhance the accuracy of lung cancer detection. Evaluation of the system's performance using a dataset of CT images and liquid biopsy results from patients shows that the combination of image processing and liquid biopsy improves the accuracy of lung cancer detection compared to using image processing alone. These findings are significant and have implications for the development of advanced systems for automated lung cancer detection that incorporate image processing and liquid biopsy." @default.
- W4387251018 created "2023-10-03" @default.
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- W4387251018 date "2023-07-29" @default.
- W4387251018 modified "2023-10-06" @default.
- W4387251018 title "Cancer Detection in Lungs Using Image Processing and Liquid Biopsy Analysis" @default.
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- W4387251018 doi "https://doi.org/10.1109/aic57670.2023.10263886" @default.
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