AI-Based Framework for Early Cancer Detection and Accurate Diagnosis in Human Patients

Abstract

Cancer continues to be one of the leading causes of death worldwide, and the challenges of late detection and misdiagnosis are major factors that hinder survival rates. This paper addresses the critical issue of diagnosing cancer at advanced stages and misclassifies it by introducing an AI-driven framework aimed at early detection and precise diagnosis in patients. We combine deep learning techniques, such as convolutional neural networks and transformers, with various clinical data sources, including medical imaging, histopathology, and genomic biomarkers. Our key findings reveal that this AI system achieves impressive sensitivity (≥90%) and specificity (≥88%) across different types of cancer, often rivalling or even exceeding the diagnostic accuracy of seasoned clinicians. This innovative approach allows for earlier detection when the disease is more treatable and helps lower the rates of misdiagnosis. The benefits of this approach include better patient outcomes, more effective treatment planning, reduced healthcare expenses, and enhanced support for clinicians through interpretable AI-assisted decision-making, establishing AI as a game-changing asset in the field of modern oncology.