AI-Enabled Computational Techniques for Cancer Diagnosis

Cancer is the world's top cause of death, expected to claim roughly 10 million lives by 2020. In clinics, medical practitioners employ a variety of approaches to diagnose cancer, such as physical examinations, laboratory tests, imaging studies, and biopsy. The critical nature of classifying cancer patients as high or low risk has prompted numerous research teams in the biomedical and bioinformatics fields to investigate the application of machine learning (ML) technologies. This research examines artificial intelligence-based methods for various cancer types. AI-enabled automated or computer-assisted approaches are chosen, but they are an excellent fit for precisely and efficiently processing a huge dataset in cancer detection. These systems allow for diagnosis and treatment. According to studies, the majority of these technologies provide exact diagnoses and can resolve the issue if used correctly. Unfortunately, these technologies must overcome a number of hurdles before they can be employed in clinics. Furthermore, data processing must be upgraded in order to be compatible with AI and machine learning. Based on the research findings, this paper concludes that AI-based learning systems offer a tremendous potential for cancer prognosis and treatment.