Artificial Intelligence in Cancer Detection : A New Era of Precision Diagnostics - Naga Lakshmi (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2025)
Artificial intelligence has revolutionized cancer detection by integrating advanced computational techniques with medical imaging and clinical data analysis. AI-driven diagnostic systems employ a systematic methodology encompassing data acquisition, image preprocessing, region of interest segmentation, feature extraction through convolutional neural networks, and sophisticated classification algorithms. These systems analyze diverse imaging modalities including mammograms, CT scans, MRI, colposcopic, and histopathological images, often achieving diagnostic accuracies exceeding 90% that match or surpass experienced clinicians. The integration of multimodal data combining imaging with genetic profiles, biomarkers, and patient histories enhances diagnostic precision and enables personalized risk assessment. AI's scalability addresses critical healthcare disparities by democratizing expert-level screening in underserved regions lacking specialized medical infrastructure. Despite challenges in data standardization, algorithmic interpretability, and regulatory approval, rigorous validation through cross-validation and clinical trials ensures safety and efficacy. AI represents a transformative partnership with human expertise, promising earlier detection, improved accuracy, and reduced global cancer mortality through accessible, consistent, and precise diagnostic capabilities.
Keywords: Artificial Intelligence; Cancer; Deep Learning; Machine Learning; Cancer Diagnosis; Oncology