A Systematic Review: Intellectual Detection and Prediction of Cancer using DL Techniques

For cancer patients to receive effective treatment, early diagnosis and accurate detection are crucial. Various phenotypes of cancer are associated with genetic and environmental factors. Pathological examinations continue to be the best tools for the diagnosis and surveillance of cancer progression. Cells and tissue samples are used to diagnose and study disease. By staging and grading tumors, the patient plays a critical role in cancer management. Cancer phenotypes are linked to both genetic and environmental variables. Pathological examinations are still the most effective methods for detecting and monitoring cancer progression. It is essential therefore that rapid, non-destructive, non-labeled imaging methods that do not require labels are developed for cancer diagnosis, which can objectively evaluate molecular compositions and sub-cellular morphological features an association between malignancy and these substances. DL gives the medical business the opportunity to observe data at exponential rates while maintaining high precision. It's neither machine learning nor artificial intelligence; rather, it's a sophisticated hybrid of the two that sifts through data at breakneck speed thanks to a multilayered mathematical design.