Enhancing Non-Invasive Cancer Detection Using Machine Learning and CNN Architectures through a Data-Driven Approach
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Abstract
Non-invasive cancer detection has emerged as a critical area of research aimed at reducing patient discomfort, improving early diagnosis, and increasing treatment success rates. This paper presents a data-driven machine learning approach that leverages Convolutional Neural Networks (CNNs) to identify cancer biomarkers from medical imaging and auxiliary biomedical data. We utilized a combination of statistical preprocessing, feature selection techniques, and supervised learning models, including CNNs and support vector machines (SVMs), to enhance diagnostic accuracy. Our experiments demonstrated that CNN-based models achieved higher sensitivity and specificity in detecting malignancies across diverse datasets. The proposed approach supports scalable, cost-effective, and clinically viable cancer detection strategies, paving the way for broader adoption in healthcare diagnostics.
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